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Production of mixed fruit (pawpaw, banana and watermelon) wine using Saccharomyces cerevisiae isolated from palm wine

Pawpaw, banana and watermelon are tropical fruits with short shelf-lives under the prevailing temperatures and humid conditions in tropical countries like Nigeria. Production of wine from these fruits could help reduce the level of post-harvest loss and increase variety of wines. Pawpaw, banana and watermelon were used to produce mixed fruit wines using Saccharomyces cerevisiae isolated from palm wine. Exactly 609 and 406 g each of the fruits in two-mixed and three-mixed fruit fermentation respectively were crushed using laboratory blender, mixed with distilled water (1:1 w/v), and heated for 30 min with subsequent addition of sugar (0.656 kg). The fruit musts were subjected to primary (aerobic) and secondary (anaerobic) fermentation for 4 and 21 days respectively. During fermentation, aliquots were removed from the fermentation tank for analysis. During primary fermentation, consistent increases in alcohol contents (ranging from 0.0 to 15.0 %) and total acidities (ranging from 0.20 to 0.80 %) were observed with gradual decrease in specific gravities (ranging from 1.060 to 0.9800) and pH (ranging from 4.80 to 2.90). Temperature ranged from 27 °C to 29 °C. The alcoholic content of the final wines were 17.50 ± 0.02 % (pawpaw and watermelon), 16.00 ± 0.02 % (pawpaw and banana), 18.50 ± 0.02 % (banana and watermelon wine) and 18.00 ± 0.02 % (pawpaw, banana and watermelon). The alcoholic content of the wines did not differ significantly (p > 0.05). The pH of all the wines were acidic and ranged from 2.5 ± 0.01 to 3.8 ± 0.01 (p > 0.05). The acid concentration (residual and volatile acidity) were within the acceptable limit and ranged from 0.35 ± 0.02 to 0.88 ± 0.01 % (p > 0.05). Sensory evaluation (P > 0.05) rated the wines acceptability as ‘pawpaw and banana wine’ > ‘pawpaw and watermelon’ > ‘pawpaw, watermelon and banana’ > ‘banana and watermelon wine’. This study has shown that acceptable mixed fruit wines could be produced from the fruits with S. cerevisiae from palm wine.

Background
Fruit juices are fermented to produce wine, an alcoholic beverage. Grapes are usually preferred because of the natural chemical balance of the grape juice which aids their fermentation process without the addition of sugars, acids, enzymes, or other nutrients. However, fruits such as banana, cucumber, pineapple and other fruits are used in wine production (Obaedo and Ikenebomeh 2009;Chilaka et al. 2010;Noll 2008).
Home-made wine production has been practiced with various fruits such as apple, pear and strawberry, cherries, plum, banana, pineapple, oranges, cucumber, watermelon, guava, etc. Using species of Saccharomyces cerevisiae which converts the sugar in the fruit juices into alcohol and organic acids, that later react to form aldehydes, esters and other chemical compounds which also help to preserve the wine (Fleet 2003;Duarte et al. 2010;Isitua and Ibeh 2010). Yeasts from other sources such as palm wine has also been used (Ayogu 1999) in the production of fruit wine.
Banana (Musa acuminata) is an important staple starchy food in Nigeria. Ripe bananas are consumed raw as a desert fruit. Banana serves as good nutritional sources of carbohydrates, minerals such as potassium and vitamins such as B 1 , B 2 , B 3 , B 12 , C and E. Following the high nutritional content of banana, it is consumed in large quantity in a variety of ways in Africa. The banana fruit can be eaten raw or cooked (e.g. deep fried, dehydrated, baked in its skin or steamed), processed into flour or fermented for the production of beverages such as banana juice, beer (e.g. mbege brewed by the Chagga people in the Kilimanjaro region of Tanzania), vinegar and wine (Pillay et al. 2004;Nelson et al. 2006;Pillay and Tripathi 2007). However, banana has a short shelf-life under the prevailing temperature and humidity condition in tropical countries, including Nigeria. This results to wastage of the fruits as a result of poor handling and inadequate storage facilities (Akubor et al. 2003;Wall 2006). Moreover, fermenting banana juice into wine is considered to be an attractive means of utilizing surplus banana, since the consumption of banana wine provides a rich source of vitamins and ensures harnessing of the fruits into a useful by-product (Obaedo and Ikenebomeh 2009).
Pawpaw (Carica papaya) is grown mostly for fresh consumption or for production of latex. C. papaya plants produce natural compounds (annonaceous, acetogenins) in leaf bark and twig tissues that possess both highly antitumour and pesticidal properties (Nwofia and Ojimelukwe 2012;Nwofia and Okwu 2012). The papaya fruit, as well as all other parts of the plant, contain a milky juice in which an active principle known as papain is present which has value as a remedy in dyspepsia and has been utilized for the clarification of beer. The juice has been in use on meat to make it tender, (Ayoola and Adeyeye 2010). The unripe fruit is used as a remedy for ulcer and impotence. It cleans bacteria from the intestines and hence encourages the absorption of vitamins and minerals, especially vitamin B 12 . The tea prepared with the green papaya leaf, promotes digestion and aids in the treatment of ailments such as chronic indigestion, overweight and obesity, arteriosclerosis, high blood pressure and weakening of the heart (Nwofia and Ojimelukwe 2012). However, ripe pawpaw fruits are very perishable, and large quantities are disposed off yearly due to lack of or poor storage facilities resulting to loss of the vital nutrients contained in the pawpaw fruits (Awe 2011; Souza et al. 2008;Nwofia and Okwu 2012;OECD 2010;Ugbogu and Ogodo 2015). However, these losses can be reduced and pawpaw can be made available all year round, by utilizing the fruits for other purposes such as wine production.
Watermelon (Citrullus vulgaris L.) is a tropical fruit which grows in almost all parts of Africa and South East Asia (Koocheki et al. 2007). It serves as a good source of vitamins and phytochemicals that have chemopreventive effects against cancer Perkins-Veazie and Collins 2004;Collins et al. 2005;Oms-Oliu et al. 2009;Enukainure et al. 2010;Inuwa et al. 2011). In Nigeria, watermelon are fermented, blended and consumed as juice, nectars, fruit cocktails and can also be used as an appetizer or snacks, depending on how it is prepared (Kerje and Grum 2003;Onyeleke and Olaniyan 2007;Oms-Oliu et al. 2009;Enukainure et al. 2010). The seeds are also reported to possess medicinal properties and are used to treat chronic or acute eczema. It contains high levels of proteins, lipids and is a rich source of carbohydrate and fibre. Arginine, glutamic acid, aspartic acid and leucine are the predominant amino acids in watermelon proteins. Reports are also available on the biological value, true digestibility, protein efficiency ratio and net protein utilisation of watermelon seeds (Wani et al. 2011;Lawal 2011;Inuwa et al. 2011). Moreover, they are used as a domestic remedy for urinary tract infection, hepatic congestion, catarrh, worm remedy, abnormal blood pressure (Amadi et al. 2003). Watermelon contain large amount of beta carotene and are significant sources of lycopene (Collins et al. 2005). The production of wine from common fruits could help reduce the level of post-harvest losses and increases the variety of wines (Okoro 2007;Alobo and Offonry 2009).
Palm wine is a refreshing alcoholic beverage widely consumed in southern Nigeria, Asia and southern America (Elijah et al. 2010). It is obtained from the sap of palm trees such as oil palm (Elaeis guiniensis) and Raphia palm (Raphia Hookeris and R. vinifera) (Okafor 2007). Palm wine is presented in a variety of flavours, ranging from sweet (unfermented) to sour (fermented) and vinegary. It is produced by a succession of microorganisms, Gramnegative bacteria, lactic acid bacteria and yeasts as well as acetic acid bacteria. Yeasts isolated from palm wine have been identified as coming from various genera such as Saccharomyces, Pichia, Schizosaccharomyces, Kloekera, Endomycopsis, Saccharomyeoides and Candida which find their way into the wine from a variety of sources including air, tapping utensils, previous brew and the trees. Hence, palm wine serves as a source of single cell protein and vitamins (Fleet 2003;Ezereonye 2004;Okafor 2007;Duarte et al. 2010; Adedayo and Ajiboye 2011). The major fermentation is undertaken by about twenty indigenous strains of S. Cerevisiae which are genetically different from the strains used to make wine from grapes and have the capability to survive and continue fermentation process up to ethanol concentration of 18 %, making them ideal for producing ethanol (Ezeogu and Emeruwa 1993;Legras et al. 2007;Noll 2008).
Though, studies have shown that bananas, pawpaw and watermelon (Obaedo and Ikenebomeh 2009;Enukainure et al. 2010;Awe 2011) and several other fruits including, pineapple (Isitua and Ibeh 2010), carrot Monsavi et al. 2011), mango (Reddy and Reddy 2005), guava (Kocher and Pooja 2011) can be used in wine production, the combination of these fruits in wine production is not readily available in literature. This paper reports the production and the quality of wine made from mixed fruits of banana, pawpaw and watermelon using S. cerevisiae isolated from palm wine.

Source of materials
Mature ripe banana (M. acuminata), pawpaw (C. papaya L), and watermelon (C. vulgaris L.) were purchased from the local central market (Nkwo Achara) in Uturu, Abia State, Nigeria. Fresh palm wine from Raphia hookeri were obtained from the palm wine tappers in Uturu within 1 h of tapping. The fruits and the palm wine were transported to the laboratory in clean cellophane bags and in an ice box respectively for analysis.

Isolation of S. cerevisiae from palm wine
Culturing of the fresh palm wine was done on Potato Dextrose Agar (PDA) and incubated at room temperature for 24 h. Nineteen isolates were obtained and subcultured on fresh medium to obtain pure cultures. The yeast cultures were transferred to modified Malt Extract Agar (MEA) containing yeast extract and 2 % glucose and then incubated for 24 h. Out of the 19 isolates, six were identified as S. cerevisiae based on their cultural characteristics, microscopy and their pattern of fermentation and assimilation of glucose, sucrose, raffinose, galactose, maltose, dextrose, trehalose and meliobiose as described by Amoa-Awua et al. (2006). The different isolates of S. cerevisiae were further screened for their ability to tolerate different concentrations of sugar and alcohol by inoculating on MEA supplemented with 10-60, and 5-30 %, sucrose and ethanol respectively. The isolate with the highest sugar and alcohol tolerance was selected and used as the starter culture. The identified organism was maintained on MEA slant.

Multiplication of starter culture
The isolated organism was multiplied prior to fermentation by culturing them on Malt Extract Broth (MEB) and incubating for 48-72 h at 27.0 °C ± 0.02. The broth cultures of the organism were centrifuged at 500 rpm for 5 min. The sediments were collected and used for must fermentation.

Preparation of must for mixed fruit fermentation
The must was prepared for two-mixed fruit and threemixed fruit fermentation respectively. The fruits were washed thoroughly with distilled water and then peeled. Exactly 609 and 406 g each of the fruit samples, banana, pawpaw and watermelon were weighed for two-mixed fruit and three-mixed fruit fermentations respectively. This was then chopped into smaller pieces using a clean knife before transferring them quantitatively into laboratory blender for crushing. The crushed sample was transferred into a clean new transparent bucket and mixed with distilled water (1:1 w/v). Exactly 0.656 kg of sugar was added to the must followed by vigorous stirring. Exactly 4 g of sodium metabisulphate (Na 2 S 2 O 5 ) was dissolved in 400 ml of water and poured in 100 ml aliquots to each of the mixtures and stirred properly. Sodium metabisulphate serve as a sterilizer and prevents fermentation before the addition of the yeast starter. The sugar concentrations were measured and the musts were mixed in the combination of 'pawpaw and watermelon' (30.4 °Brix), 'pawpaw and banana' (29.3 °Brix), 'pawpaw, banana and watermelon' (32.1 °Brix) and then 'banana and watermelon' (31.2 °Brix).

Preparation of yeast starter culture
The yeast starter culture was prepared from a known quantity of the must for fermentation, small quantity of sugar, yeast and a known volume of water. The mixture of all these were treated with yeast nutrients and allowed to stand for 24 h. Approximately 200 ml of water was boiled and allowed to attain 37 °C and 200 ml of each mixture of the must (banana and pawpaw, banana and watermelon, pawpaw and watermelon, and banana, pawpaw and watermelon) respectively treated with sugar was added. Exactly 5 g of citric acid was added to each of the preparations and then stirred for proper mixing. Exactly 2 g each of the yeast nutrient namely Potassium phosphate, Ammonium sulphate and Magnesium sulphate was dissolved in 100 ml of distilled water and poured to each of must mixture. Exactly 3.7 ml representing approximately 10 8 cfu/ml (measured using McFarland standard) of the yeast (S. cerevisiae) isolated from palm wine after centrifugation was added to each of the mixture, stirred properly and allowed to stand for 24 h before use.

Fermentation
The primary fermentation was initiated by the addition of the starter culture. The must was stirred every 12 h with subsequent reading of the specific gravity, pH, temperature and alcohol content for 4 days. After 4 days, the wine was racked into the secondary fermenter. The secondary fermentation was done in an air tight container in which a tube was passed into a clean bottle containing clean water. The essence was to monitor the course of fermentation. This was allowed until completion of fermentation as was evidenced by lack of the appearance of bubbles in the container usually within 3 weeks. Secondary fermentation was done for 21 days. When fermentation stopped, the wine was promptly racked off the lees ensuring minimum exposure to oxygen. After secondary fermentation, the wines were clarified. The clarification/fining were done using bentonite (a clarifying agent). Exactly 500 g of bentonite was dissolved in two litres of boiling water and stirred properly to a gel form. This was allowed to stand for 24 h. Then 150 g of the gel-like bentonite was transferred into each of the wine followed by stirring to dissolve properly. A small quantity of the mixture was collected in a clean bottle which was covered tightly and was used to monitor the process of clarification. This was done for a period of 3 months. Filtration was done after the wines had completed clarification using muslin cloth, sieve and syphon tubes sterilized by 70 % alcohol. The wines was syphoned into the sieve containing four layers of muslin cloth. The residues were removed and the filtrates were allowed to mature for a period of 6 months before other chemical analysis was carried out.

Isolation of microorganisms from the fermentation broth
Microbial analysis of the fermentation broth was performed as described by Fleet (2003) using Nutrient Agar (NA), MacConkey Agar (MA) and Potato Dextrose Agar (PDA). The nutrient agar used was treated with fulcin (50 mg/20 ml of NA) to suppress fungal growth while the PDA was treated with chloramphenicol. The cultured plates were incubated at room temperature and pure cultures were obtained by streaking and identified based on colonial characteristics, microscopy, biochemical reactions and carbohydrate utilization (Fawole and Oso 1988;Onyeagba 2004). The fungi were identified only on the basis of their cultural characteristics and microscopy (Isitua andIbeh 2010 Barnett et al. 2000).

Chemical analysis of the wines
The volatile acidity was determined using the method described by McClements (2003), total acidity of the wines was determined by titration and concentration of the acid was calculated. The residual acidity of the wines was also determined as described by McClements (2003) while the alcohol content was determined using the density method. The specific gravities of the wines were determined using the hydrometer method and the results were determined from the reading on the stem (Awe 2011). The total solid and total sugar content of the wines were determined using the method of McClements (2003) and the pH and temperature were determined using a digital pH metre and an analytical thermometer respectively.

Sensory evaluation
The wines produced were compared for colour, flavour, taste, clarity, and overall acceptability by a panel of twenty judges on a seven point hedonic scale where seven denotes excellent and one very poor.

Statistical analysis
The completely randomized analysis of variance (ANOVA) was used as described by (Winner 2004) to analyze the data obtained. Mean separation and comparison was done using SPSS version 16.0. Significance was accepted at P < 0.05 and results were expressed as mean ± standard deviation from the mean.

Results
The morphological and physiological characteristics of the yeast isolated from palm wine are represented in Table 1.
There were fluctuations in the temperature of the mixed fruit wines throughout the period of fermentation (Fig. 1). These variations were observed for all the wines. In all the mixed fruit wines, the temperatures were observed to range from 27.0 °C ± 0.02 to 29.0 °C ± 0.02. The pH in the mixed fruit wines was acidic throughout the period of fermentation. This was also irrespective of the fruit wine. The pH ranged from 4.0 ± 0.01 to 4.8 ± 0.01 in pawpaw and watermelon wine, 2.9 ± 0.01 to 3.8 ± 0.01 in pawpaw and banana wine, 3.4 ± 0.01 to 4.0 ± 0.01 in pawpaw, banana and watermelon wine and 3.6 ± 0.01 to 4.3 ± 0.01 in banana and watermelon wine (Fig. 2).
A steady increase in alcohol content was observed in the mixed fruit wines throughout the period of primary fermentation (Fig. 3). This increase was observed in all the mixed fruit wines irrespective of the fruits used. The concentration of alcohol in the mixed fruit wines at the end of primary fermentation were observed to range from 0 to 15, 0 to 14, 0 to 15.5 and 0 to 15 % in pawpaw and watermelon wine, pawpaw and banana wine, pawpaw, banana and watermelon wine and banana and watermelon wine respectively. The highest alcohol content was observed in the wine produced by the mixture of pawpaw, banana and watermelon (15.5 %), while the least alcohol content was observed in pawpaw and banana wine (14 %). The specific gravities of the mixed fruit wines gradually decreased throughout the period of primary fermentation. After primary fermentation, specific gravity values were observed to range from 0.9800 to  1.0600, from 0.9820 to 1.0300, from 0.9800 to 1.030 and from 0.9810 to 1.0900 in pawpaw and watermelon wine, pawpaw and banana wine, the three mixed fruit wines and banana and watermelon wine respectively. Figure 4 showed the trend in total acid concentrations in the mixed fruit wines during the primary fermentation period with the test yeast. As shown in the figures, total acidity was observed to show steady increase with time throughout the period of primary fermentation. These increases were irrespective of the test fruit wine. At the end of primary fermentation, acid concentration in the pawpaw and watermelon wine was observed to increase from initial concentration of 0.20 ± 0.01 to final concentration of 0.32 ± 0.02 %. Similarly, total acidity was observed to increase from initial concentration of 0.40 ± 0.02 to a final concentration of 0.80 ± 0.02 %, 0.41 ± 0.01 to 0.71 ± 0.01 % and 0.29 ± 0.02 to 0.62 ± 0.01 % for pawpaw and banana wine, the three mixed fruit wine and banana and watermelon wine respectively.
After secondary fermentation, the temperature of the wines were observed to range from 27 ± 0.07 °C for pawpaw and banana wine to 28 ± 0.07 °C for pawpaw, banana and watermelon wine and banana and watermelon wine. The pH of the wines maintained an acidic range of 2.7 ± 0.1 for pawpaw and banana wine to 3.9 ± 0.1 for pawpaw and watermelon wine. There were little increases in the alcoholic content of the mixed fruit wines after secondary fermentation. The alcohol content of pawpaw and watermelon wine increased from 15 ± 0.02 % in primary fermentation to 16.5 ± 0.02 % after secondary fermentation, 14 ± 0.02 to 15.2 ± 0.02 %, 15.5 ± 0.02 to 17.5 ± 0.02 % and 15 ± 0.02 to 18 ± 0.02 % for pawpaw and banana wine, pawpaw, banana and watermelon wine and banana and watermelon wine respectively. The highest alcohol content was observed in banana and watermelon wine (18 ± 0.02 %) while pawpaw and banana wine recorded the lowest alcohol content (15.2 ± 0.02 %). In the case of specific gravities, little decreases were also observed in all the wines after secondary fermentation with banana and watermelon wine having the lowest value (0.9770 ± 0.00) and, pawpaw and banana wine having the highest value (0.9800 ± 0.00) while the acid concentrations ranged from 0.34 ± 0.02 (pawpaw and watermelon wine) to 0.86 ± 0.02 % (pawpaw and banana wine) ( Table 2). The general chemical parameters of the mixed fruit wines after maturation compared favourably. The result indicated that the final alcohol concentration of pawpaw and watermelon wine, pawpaw and banana wine, pawpaw, banana and watermelon wine, and banana and watermelon wine, were 17.50 ± 0.02, 16.00 ± 0.02, 18.00 ± 0.02 and 18.50 ± 0.02 % respectively (Table 3). These variations do not show any significant difference (p > 0.05).
Sensory evaluation (p > 0.05) rated the acceptability of the wines as pawpaw and banana wine > pawpaw and watermelon > pawpaw, watermelon and banana > banana and watermelon wine (Table 4).

Discussion
The fermentation of wine is known to be complex with various ecological and biochemical processes involving yeast strains (Fleet 2003). The fermentation for the elaboration of beverage is known to depend on the performance of the yeast to convert the sugars into alcohol and esters. Besides, the different species of yeast that develop during fermentation determine the characteristics flavour and aroma of the final product (Duarte et al. 2010). Also, because different fruits have different composition, there is the need for yeast strains to adapt to different environments, such as sugar composition and concentration of acetic acid (Fleet 2003;Chilaka et al. 2010;Duarte et al. 2010).

Table 2 Temperature, pH, specific gravity, alcohol content and total acidity of the wines after secondary fermentation
Values are expressed as mean ± standard deviation A pawpaw and watermelon wine, B pawpaw and banana wine, C pawpaw, banana and watermelon wine, D banana and watermelon wine, % percentage, Temp Temperature

Wines
Temp (   The mixed fruit wines (pawpaw and watermelon wine, pawpaw and banana wine, pawpaw, banana and watermelon wine and banana and watermelon wine) produced in the present investigation revealed low pH values (in the range of 2.5-3.8) throughout the fermentation periods and in the final product. Similar observations have been reported for other tropical fruit wines such as tundu wine (Sahu et al. 2012), sweet potato wine (Ray et al. 2011), sapota fruit wine (Panda et al. 2014a, b) and banana wine (Obaedo and Ikenebomeh 2009). Studies have shown that during fermentation of fruit, low pH is inhibitory to spoilage organisms but increases conducive environment for the growth of desirable organisms. Also, low pH is known to give fermenting yeasts a competitive advantage in natural environment (Reddy and Reddy 2005;Chilaka et al. 2010). The decrease in pH could be due to accumulation of organic acids during fermentation and this reduces the influence of bacteria that can lead to spoilage. Therefore the wines have a good keeping quality.
Fluctuations in temperature of the must were observed during the period of fermentation. This could be as a result of biochemical changes occurring during the metabolism of the substrates by the fermenting organism. Temperature of the final mixed fruit wines ranged from 27.00 ± 0.07 to 28 ± 0.07 °C.
The present study also revealed a consistent increase in the total acidity of the mixed fruit wines throughout the period of fermentation. The total acidity of final wine is expected to be between 0.5 and 1.0 % (Chilaka et al. 2010). In this study, the result of the total acidity in the mixed fruit wines fell within this limit ranging from 0.35 ± 0.02 to 0.88 ± 0.01 %. However, the acidity is lower than the reports of Ray et al. (2011) for sweet potato wine (1.34 g/100 ml) and Panda et al. (2014a) for sapota fruit wine (1.29 g/100 ml) but is consistent with the report of Panda et al. (2014b) who reported 0.15 ± 0.07 g/100 ml for bael wine. High acidity is known to favour the fermentative and competitive advantage of yeasts in natural environment as reported by Reddy and Reddy (2005). This acidity was observed to be more of volatile acidity than the residual acidity. This implies that even if the wines are consumed in large quantities, the acidity level can easily be removed by the body system. Moreover the acidity (volatile and residual) of the wines in the present study do not differ significantly (p > 0.05).
In order to supplement the sugar content of the musts, sucrose was part of the additives. Reports have shown that the major problem associated with the use of tropical fruits in wine production is their low sugar contents (Alobo and Offonry 2009). In the present study, the fermentation was nearly complete with total sugar content of 0.76 ± 0.02, 0.94 ± 0.02, 0.64 ± 0.02, and 0.54 ± 0.02 % in 'pawpaw and watermelon wine' , pawpaw and banana wine' , 'pawpaw, banana and watermelon wine' and banana and watermelon wine respectively. This observation did not correspond with the reports of Panda et al. (2014a), Ray et al. (2011), Sahu et al. (2012 and Panda et al. (2014b) who reported higher values for sapota fruit wine (3.28 g/100 ml), purple sweet potato wine (1.35 g/100 ml), tendu wine (3.78 g/100 ml) and bael wine (2.05 ± 0.12 g/100 ml) respectively. The result revealed that the total sugar contents of the wines in the present study are less than 1 %. This is an indication that the wines will have a good keeping quality since the fear of further fermentation during storage which could lead to spoilage will not arise. This result also showed that the wines could be classified as dry table wines because of low total sugar content of less than 1 %. The variations in the total sugar content of the wines were not observed to differ significantly (p > 0.05).
The total solids obtained in the wines were low ranging from 0.16 ± 0.02 % to 0.46 ± 0.02 in pawpaw and watermelon wine and pawpaw and banana wine respectively, and do not differ significantly (p > 0.05). This could be attributed to the efficiency of the yeast in fermentation. It also implies that consumers are not exposed to the risk of taking in too much solid into the body. However, reduction in the total solid of the wines could be achieved by further filtrations.
Remarkable amount of alcohol were produced from the fruit wines during fermentation with the test yeast (S. cerevisiae from palm wine). This trend was consistent in all the wines. In general, the percentage alcohol produced from the respective mixed fruit wines at the end of fermentation by the test yeast were 17.50 ± 0.02 % (pawpaw and watermelon wine), 16.00 ± 0.02 % (pawpaw and banana wine), 18.00 ± 0.02 % (pawpaw, banana and watermelon wine) and 18.50 ± 0.02 % (banana and watermelon wine). This finding agree with the work of  Noll (2008) reported that the strains of the yeast (S. cerevisiae) isolated from palm wine are different genetically from the yeast strains that are used to make wine from grapes and have the ability to survive and continue fermentation process up to ethanol concentration of 18 %, making them ideal candidate for producing ethanol for fuel. The performance and potential of the test yeast as substitute for commercial baker's yeast was measured by the amount of alcohol produced. The alcohol produced by the test yeast in this study were high (16.00-18.50 %) compared to the studies on commercial yeast (10.46 %) as reported by Chilaka et al. (2010). High alcohols are known to be important precursors for the formation of esters, which are associated with pleasant aromas Clement-Jimenez et al. 2005). Reports have shown that alcoholic fermentation leads to a series of by-products in addition to ethanol. Some of the by-products include carbonyl compounds, alcohols, esters, acids and acetyls. All of which influence the quality of the finished product. The composition and concentration levels of the byproducts can vary widely (Duarte et al. 2010). In general, the concentrations of ethanol contribute to the whole characteristic quality and flavour of the produced wine Reddy 2009, 2004). However, the amount of alcohol produced by the test yeast were not observed to differ significantly (p > 0.05).
In the present investigation, the test fermentation yeast (S. cerevisiae) was the only organism isolated from pawpaw and watermelon wine as well as pawpaw and banana wine while neither pawpaw, banana and watermelon wine nor banana and watermelon wine showed the presence of any microorganism. This is an indication of good quality. This observation may be attributed to low pH values, high acidity and high alcohol contents of the wines which are known to inhibit the growth of pathogens and gives fermenting yeast a competitive advantage in natural environment as reported by Reddy and Reddy (2005) and Chilaka et al. (2010). The absence of the growth of the yeast in pawpaw, banana and watermelon wine and banana and watermelon wine could be due to the high alcoholic content which exceeded the ethanolic tolerance level of the yeast used for fermentation.
The colours of the wines in the present study were observed to be pale yellow (pawpaw and watermelon wine), straw yellow (pawpaw and banana wine), dark brown (pawpaw, banana and watermelon wine) and cream (banana and watermelon wine). This is an indication that the combination of the fruits served as a good substrate for wine production with pawpaw and banana being the most efficient as shown in this study. The good aroma obtained in the wines could be attributed to high alcohol content in accordance with the report of Clement-Jimenez et al. (2005).
Sensory evaluation rated the wines acceptability as pawpaw and banana wine > pawpaw and watermelon > pawpaw, watermelon and banana > banana and watermelon wine. These attributes compared favourably with the reports for other tropical wines (Akubor et al. 2003;Ray et al. 2011;Sahu et al. 2012;Panda et al. 2014a, b). Also, the sensory evaluation of the wines in the present study do not differ significantly (P > 0.05).

Conclusion
The present study which was based on the evaluation of three indigenous fruits as substrates for wine production and the efficiency of isolated S. cerevisiae from palm wine for mixed fruit wine production has revealed that the three test fruits (pawpaw, banana and watermelon) are good substrates for wine production. The biochemical and sensory attributes of the wines were acceptable by the consumers. The study has also given an insight into the efficacy and role of S. cerevisiae from palm wine during alcoholic fermentation of fruits. Pawpaw, banana and watermelon have short shelf-life under the prevailing temperature and humidity condition in Nigeria. Therefore, this study provides an avenue to preserve their nutrients, minerals, vitamins, aroma and taste to the consumers by fermenting them into wines.


extfieldsofstudy: Mathematics Medicine

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Penaeus orientolis prawn freshness rapid determination method based on electronic nose and non-linear stochastic resonance technique

In this paper, Penaeus orientolis prawn freshness rapid determination method using electronic nose (e-nose) and non-linear data processing technique is studied. E-nose responses to prawns stored at 4°C are measured. Meanwhile, physical/chemical indexes (firmness, pH, total volatile basic nitrogen (TVB-N), total viable count (TVC), and human sensory evaluation) are examined to provide freshness references for e-nose analysis. E-nose measurement data is analyzed by principal component analysis (PCA), stochastic resonance (SR), and double-layered cascaded serial stochastic resonance (DCSSR). PCA partially discriminates prawns under different storage time. SR and DCSSR signal-to-noise ratio (SNR) spectrum eigen values discriminate prawns successfully. Multi-variables regressions (MVR) are conducted between physical/chemical indexes and SR/DCSSR output SNR minimal (SNR-Min) values. Results indicate that SNR-Min values present more significant linearity relation with physical/chemical indexes. Prawn freshness forecasting model is developed via Harris fitting regression on DCSSR SNR-Min values. Validating experiments demonstrate that forecasting accuracy of this model is 94.29%.

Introduction
Penaeus orientolis prawn is particularly rich in protein, essential amino acids, and essential minerals (Fe, Zn, Se, etc.). It is now the most important shrimp cultivated in the world in terms of volume. It contains rich nutrients such as protein and micronutrients necessary for human body. However, it is hard to preserve and usually deteriorates in a few days. And protein, the main component of white shrimp, will produce toxic amine compounds such as trimethylamine, etc., after deterioration. The intake of deteriorated prawns brings great harm to human body, which has been one of the most serious problems in food safety area. Traditional aquatic products quality analysis methods mainly include instrumental analysis, physical/chemical examination, and human sensory evaluation. Instrumental analysis methods, such as VIS/NIR spectroscopy, gas chromatography-mass spectrometry (GC-MS), and impedance spectroscopy, are usually utilized for aquatic products freshness detection under laboratory conditions. 1-3 However, these methods have some disadvantages, such as high cost, time-consuming, etc. Moreover, skilled operators are required to perform instrumental analysis. Physical/ chemical examination methods, such as conductivity/pH measurement and TVB-N, can examine freshness of aquatic products according to relative standards. 4,5 However, these methods present some disadvantages, such as high cost, time-consuming, low reproducibility, etc. Moreover, skilled operators are required to perform the instrumental analytical experiments. So a convenient, fast and robust method is badly needed for prawn freshness determination.
E-nose, imitating human olfactory sense, is a kind of electronic instrument. 6,7 In the past 2 decades, e-nose has been successfully applied in diverse fields such as food quality analysis, environmental control, industrial applications, and clinical diagnostics. Pattern recognition (PR) methods used by e-nose can be divided into supervised and non-supervised methods although a combination of both can be used. 8,9 The major supervised techniques are linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN), artificial neural networks (ANN), and classification and influence matrix analysis (CAIMAN). Unsupervised techniques include cluster analysis (CA), artificial neural networks (ANN) and PCA. 10 In 2001, a portable electronic nose was used to detect fish freshness by estimating the number of storage days. 11 An electronic nose system was built based on metal oxide semiconductor (M.O.S) sensors to evaluate freshness of sardines under cold storage. 12 Results showed that the sensor signals were well correlated with the number of days that sardines had undergone. Discriminant factor analysis (DFA) and fuzzy ARTMAP neural networks were used to build classification models. Limbo et al. applied chemical methods and electronic nose to predict the decay and shelf life of European sea bass. 13 A new optoelectronic nose was developed to monitor shelf-life of fresh sea bream under cold storage. 14 In 2004, this group evaluated freshness of Atlantic salmon using optoelectronic nose. 15 Hui et al. predicted grass carp quality by an e-nose combined with non-linear SR. 7 The system presented good predicting accuracy for grass carp samples.
In this paper, prawn freshness rapid analysis method based on e-nose technique and non-linear SR is studied. Prawns stored at 4 C under different storage time were measured by e-nose. Physical/chemical indexes (firmness, pH, TVB-N, TVC, and human sensory evaluation) are examined in order to characterize prawns' freshness. E-nose measurement data is processed by PCA, SR, and DCSSR. MVR results demonstrate that DCSSR eigen values present more significant linearity relation with physical/chemical indexes. Prawn freshness forecasting model is developed based on Harris fitting regression of DCSSR SNR-Min values. Validating experiments demonstrate that the developed model forecasts prawn freshness with an accuracy of 94.29%.

Results and Discussions
Firmness Prawn firmness examination results are displayed in Figure 1a. Firmness increases slightly in day 2. After day 2, firmness keeps declining and reaches less than 70N in day 6 finally. A possible explanation for this phenomenon lies in the changes of ATP content within shrimps during cold storage. ATP determines the state of actin and myosin. After prawns' death, glide between actin and myosin happens because of ATP decomposition with the increase of storage days, which leads to muscle stiffness. After reaching stiffness maximal value, endogenous proteases within muscles and exogenous proteases induced by spoilage bacteria make Z line of myofibrillar more weak or broken. So prawns' muscle becomes soft, which leads to the decline in firmness.
pH Prawn's inner chemical environment changes with the decrease of its quality during storage. pH measurement results are displayed in Figure 1b. pH value is about 7.2 in day 0, and then declines in day 1. From day 2 to day 8, pH keeps increasing. During storage, prawns undergo anaerobic glycolysis procedure. Glycogen degradation and lactic acid formation lead to the rapid decline of pH. When the prawns get deteriorated, trimethylamine oxide within the samples is reduced to trimethylamine with the help of microbes. The formation of trimethylamine leads to slight decline of pH. During further storage, protein within prawns decomposes and releases alkaline substances (ammonia and amines, etc.), which increases pH from day 2 to day 8.

TVB-N
The essential of prawn freshness changes is that the protein decomposes into various chemical substances and metabolites by function of microorganisms. TVB-N index effectively characterizes this decomposing procedure. TVB-N examination results are shown in Figure 1c. TVB-N increases gradually with the increase of storage days. The contents of TVB-N in the first 3 days are 16.50 mg/100 g, 18.60 mg/100 g, and 19.20 mg/100 g, respectively, which accords to China national standard (<20.00 mg/ 100 g). 16 The results demonstrate that prawns from day 0 to day 2 are fresh and edible. However, TVB-N content of prawns in day 3 is about 20.10 mg/100 g, which exceeds GB/T 5009.44-2003. After day 3, the content of TVB-N increases dramatically, and reaches 39.00 mg/100 g in day 8. The results indicate that prawns from day 3 to day 8 are not fresh.

TVC
Microbial propagation is a crucial factor influencing quality of aquatic products. During storage, prawns deteriorate gradually due to microbiology propagation. Microbiological metabolism leads to emission of feature gases, which provides measurement basis of e-nose analysis. So it is important to trace the microbiological propagation on prawns. TVC values of samples are monitored and displayed in Figure 1d. Initial TVC value is about 2.1£10 4 cfug ¡1 . The TVC value increases dramatically fast after day 3. TVC value in day8 is about 2.2£10 7 cfug ¡1 .
Human sensory evaluation Sensory attributes of prawn samples were divided into 5 elements, whose preference scores were scored from 1 to 5; the higher preference score, the better quality of prawn (see Table 1). All samples started with score of 5 in day 0.
In Figure 1e, preference scores decrease with the increase of storage days. Choosing 3.5 as the acceptable limitation, the prawns can maintain its shelf life by 3 days, and the preference score in day 3 is about 3.39. Sensory evaluation results accord to TVB-N results well. Combined with TVB-N and TVC examination results, sensory evaluation results might be affected by microbial activities and changes of prawns' self components.

Original responses and PCA results
In e-nose measurement, gas sensors are subjected to time drift. E-nose's stability is evaluated by conducting blank sample measurement. Zero gas, obtained by filtering air through active carbon, is used as blank sample. E-nose responses to blank sample are measured for 7 days. Blank sample measurement results demonstrate that e-nose responses to zero gas remain stable. Meanwhile, the sensors' time drift during measurement has no effect to e-nose measurement. E-nose responses prawns are recorded. Figure 2a shows e-nose gas sensor responses on day 7. With the increase of measurement time, 8 sensors' responses increase gradually. The responses of the sensors to sample's headspace gases are similar. Obvious increase occurs in the time interval between 2.5 s and 25 s. Finally, the responses trend to be stable. Sensor S4, S1, S5, S7, S6, S8, and S3 show high responsiveness to all the samples. S4 shows the largest responses. Responses of S2 are much smaller. Gas sensors within e-nose present various responses to prawns, which constructs the analysis basis of e-nose. Figure 2b presents the PCA results. The first 2 principal components, PC1 and PC2, capture 94.5% of data variance. The figure shows the projections of PCA results on a 2-dimensional plane formed by the first 2 principal components. Combined with TVB-N examination results, prawns can be classed into 2 groups: fresh group and non-fresh group. Fresh group corresponds to the samples having undergone up to day 2. Non-fresh group corresponds to the samples from day 3 to day 6. From day 0 to day 2, the samples gather in the first and forth quadrant. Day 3 is the turning point. From day 3 to day 8, PC1 decreases quickly, and PC2 first decrease from day 3 to day 4. From day 4 to day 5, PC2 dramatically increases. In day 6, PC2 decrease slightly. However, Fresh and non-fresh samples have no obviously discriminating border. The samples under different storage time within the same group cannot be clearly discriminated from each other. PCA only provides a qualitative analysis on e-nose measurement data. PCA results on a 3-dimensional plane formed by the first 3 principal components are displayed in Figure 2c. The first 3 principal components, PC1 and PC2, capture 97.44% of data variance. All prawns under different storage time still cannot be totally discriminated from the 3-dimensional angle.

SR and DCSSR SNR spectrum
Prawn e-nose measurement data SNR spectrum calculated by SR as function of external white noise intensity is displayed in Figure 2d. A derivative vale arises at noise intensity of 37. Then SNR increases gradually with the increase of external noise intensity. Eigen peak appears at noise intensity of 65. The SNR maximal (SNR-Max) values range between ¡82 and ¡79 dB.

SNR-Max values increase with the increase of storage time. Prawn samples under different storage days can be discriminated by SNR-Max values.
Prawn e-nose measurement data SNR spectrum calculated by DCSSR as function of noise intensity are displayed in Figure 2e. A derivative vale arises at noise intensity of 37. SNR increases gradually with the increase of stimulating noise intensity. A derivative peak appears at noise intensity of 74. Then SNR gradually decreases, and reaches its minimal value at noise intensity of 118. SNR-Min values range between ¡74 and ¡53 dB. SNR-Min values decrease with the increase of storage time. Prawns of different storage time can be clearly discriminated according to SNR-Min values. Compared with SR output SNR spectrum, DCSSR method especially improves output signal intensity, which contributes to prawn discrimination using SNR eigen values.

MVR between physical/chemical indexes and SR/DCSSR eigen values
Quality is a general description for prawns. The physical/ chemical indexes, such as TVB-N, TVC, pH, firmness, have close relationship to prawns' quality. MVR results between physical/chemical indexes and SR SNR-Max values are displayed as Equation (1) and Table 2. R 2 D 0:99741 indicates that SR eigen values have good linearity with prawn quality indexes. P < 0.05 and F D 77.1599, which indicate that SNR-Max values present significant linearity relation with physical/ chemical indexes.
MVR results between physical/chemical indexes and DCSSR SNR-Min values are displayed as Equation (2) and Table 3.

Validation experiment results
Validating experiments are carried out to examine predicting accuracy of the developed model. Equation (4)

Prawn samples
Fresh prawns, purchased from Wen'er aquatic market in Hangzhou, were transported to laboratory in ice box. 280 samples were prepared and packaged with sealing membrane and stored at 277 K for 7 days. Each sample weighs about 25 g. In each day, 40 samples were used for sensory preference, firmness, TVB-N, TVC examination, and e-nose measurement.
In validating experiments, 84 new prawns were selected and stored for 7 days. In each day, 12 samples were used for e-nose measurement. All samples were taken randomly.  China). Deionized water was used in all experiments. All chemicals were of analytically pure grade or better quality.

E-nose system
Prawn samples were measured by a portable e-nose. The structure is displayed in Figure 3a They consist of ceramic substrate coated by metal oxide semiconducting film, and heated by wire resistor. Due to the high temperature (250-500 C), the volatiles transferred to the surface of the sensors are totally combusted to carbon dioxide and water, leading to a change in the resistance. The high temperature avoids water interference and provides sensors fast response and rapid recovery time. Polytetrafluoroethylene (PTFE) material is utilized to fabricate the chamber. Each sensor room is separated, which helps to avoid the cross-influence of the gas flow.

Firmness
TA.XT2i Texture Analyzer (Stable Micro Systems, UK) is used to conduct firmness measurement. Flat cylindrical probe p/5 (5 mm in diameter) was used. TPA mode was accepted. Speed in pre-measurement and after-measurement was 3 mm/s. Measurement speed was 1 mm/s. Compression degree was 50%. Residence time interval was 5 s. Load probe type was Auto-0.2 g. Data collection rage was 200.
pH In pH measurement, 10 g prawn was smashed by a muller. Deionized water was added into the smashed sample to fully dissolve chemical substances. The sample and water were poured into conical flask. Muller was washed by deionized water, and washing liquid was also poured into conical flask.
The mixture in the conical flask was finally diluted to 100 mL, and left to stand for a while. Then the mixture was filtered and measured by PHS-3C pH meter.

TVB-N
In TVB-N examination, magnesium oxide method was used in which contain (ammonia, mono-methyl amine, diethyl amine and tri-methyl-amine and other volatile amine). TVB-N examination was conducted by micro kieldahl apparatus (Hongji Instruments Company, Shanghai, China). The prawn meat was cut by pulper (Yide Electric Appliance Co., Ltd., Guangdong, China). According to China standard protocols GB/T 5009.44-2003 "Method for analysis of hygienic standard of meat and meat products," 16 10 g sample was added to the heating flask containing 100 mL deionized water. After 30 minutes, the mixture was filtered. The filtrate was placed into refrigerator. 10 mL filtrate and 5»6 drops of indicator were mixed and added into a heating flask. 5 mL of mixture and 5 ml magnesium oxide suspension (10 g/L) were added into distiller. After 5 minutes' distill, hydrochloric acid solution (0.01 mol/L) was used for titration. The TVB-N was determined as follows: Where: X was the TVB-N index in mgN/100 g. V 1 was the volume of hydrochloric acid used in sample titration in mL. V 2 was the volume of control used in titration in mL. c was the concentration of hydrochloric acid in mol/L. m was the weight of sample in grams.
TVC 25 g of prawn meat was aseptically weighted and homogenized with 225 ml sterile 0.1% physiological saline for 1 min. The homogenized sample was serially diluted using 9 ml sterile saline for bacteriological analysis. TVC was determined in plate count agar by the spread plate method. 17

Sensory evaluation
According to previously reported method by Amerine et al., 18 the sensory of prawn samples was evaluated by 6 experienced panelists (2 males and 4 females). Voting number is set at k, k 2 (1,10). The quality of prawn is divided into m levels, and the score of a specific level is set at h, j 2 (1, m). Prawn attributes are divided into n elements, and a specific element is set at u i , i 2 (1,n). The contributory weight is determined by pairwise comparison of contribution weight of attributes is set at x i ( P x i D 1). If there is a specific relationship between 2 objects of h and u i , the relation set (matrix) of f is calculated as follows: Thus, the overall acceptability of prawn is calculated by the weight grade method as follows: E-nose measurement In e-nose measurement, 25 g of samples were placed into 50 mL glass air-tight vials, and sealed with sealing membrane. The vials were stored at 277 K until analysis. The vials were equilibrated at room temperature for 45 min and analyzed in standardized conditions. After the power is turned on, washing pump and valve 2 are started, and sampling pump and valve 1 are close. The air is filtered by active carbon to obtain zero gas. Sensor array are washed by zero gas. When sensors' response reach the baseline, the washing pump and valve 2 are closed. Then the sampling pump and valve 1 are started. The e-nose sucked the headspace gases of samples through the sensor array at 400 mL/min for 40 s. The measurement interval of e-nose was 0.05 s. When measurement is finished, the sensors are washed by zero gas for 600 s at a flow rate of 1000 mL/min prior to the next sample measurement.
Quality predicting method SR SR was proposed by Benzi to give an explanation for Earth climate periodical changes. 19-24 SR model can be described as follows: Where x is the position of the Brownian particle, t is the time, M and C are adjustable parameters, I.t/ D S.t/ C N .t/ is an input signal S.t/ with an intrinsic noise N .t/, j.t/ is the external noise, and V .x/ is the simplest double-well potential with the constants a and b.
V .x/ D ¡ 1 2 Equation (8) can be transformed as The minima of V .x/ are located at § x m , where x m D .a=b/ 1=2 . A potential barrier separates the minima with the height given by DU D a 2 =4b. The barrier top is located at x b D 0. When three elements of SR interact coherently, the potential barrier can be reduced and the Brownian particle may surmount the energy barrier and enter another potential well. The intensity of signals will increase, which makes it possible that the weak signal can be detected from noise background.
Suppose the input signal is I.t/ D Asin.2pft C '/, where A is signal intensity, f is signal frequency. D is external noise intensity. SNR is the common quantifier for SR and it can be approximately described as: Noise intensity is a parameter in SR model. This model is used for e-nose data analysis. I.t/ D Asin.2pft C '/ C EN .t/ C N .t/ denotes an input matrix. It has a sinusoid signal Asin.2pft C '/, electronic nose response data EN .t/, and intrinsic noise N .t/. SNR between the output and input is calculated. This model has been successfully used in food analytical applications. [19][20][21][22]25 The schematic diagram of SR is showed as Figure 3b.

DCSSR
Under adiabatic elimination condition, supposing signal amplitude is much smaller (Ahh1), the Brownian particle is in one of potential wells because no enough driving energy is provided by the bistable system to drive the particle to jump from one potential well to another one in the absence of external noise. The signal period is longer than some characteristic intrawell relaxation time for the system. The existence of periodic forcing inclines the potential function, and forms the Brownian particle's transfer from one potential well to another one. So the potential function V .x/ changes with the input signal and becomes Equation (12) indicates the potential function gets time dependence. Equation (13) displays the first-order and secondorder derivation of V .x; t/ with respect to x, and let the equations equal to zero: Setting noise intensity D D 0 and sin.2pft C '/ D 1, the critical amplitude value of the periodic signal can be obtained: A c D ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 4a 3 =27b p . If A < A c , the particle hovers around its original stable state and cannot jump from one potential well to another one. However, the particle can jump from one potential well to another one with the help of external noise even if A < A c , which means the occurrence of SR phenomenon. We use fourth-order Runge-Kutta numerical algorithm to solve Equation (13): x is the nth numerical value of x.t/, and sn n is the nth numerical value of S n .t/. h is the computation step. Much progress has been achieved on the applications of SR in the past few decades.
Single SR system connected in series forms the cascaded SR to obtain DCSSR (see Figure 3b). According to Equation (10), the relative Langevin equations of the cascaded bistable systems can be respectively written as: In practical engineering measurement, measured data usually consists of signal and intrinsic noise. If an aimed weak signal is submerged in strong noise, we are not able to detect it. With the help of SR, the energy of intrinsic noise is lowered, and the embedded weak signal is amplified effectively so that the signal can be caught for measurement characterization. In some special measurement, the weak signal is so weak that the aimed signals are still embedded in the noise. DCSSR is designed to solve this problem. For a DCSSR system, several bistable systems are in serial. If the measurement signals are still embedded in the noise, the output of the former bistable system is taken as the latter bistable system. If the aimed signals are successfully obtained, the current number of bistable systems is the suitable for the information measurement. The schematic diagram of DCSSR is showed as Figure 3c.
Mechanism analysis of DCSSR From above results, external white noise plays an important part in e-nose measurement data feature extraction. What is acting mechanism between external nose and weak feature signal? How can e-nose signals be effectively strengthened? Here, the weak signal amplification efficiency is theoretically evaluated. According to Equation (9), Equation (8) can be obtained: In SR analysis, we suppose the initial intrinsic noise signal N .t/ is twice the e-nose signal enost.t/ strength. Then . In DCSSR analysis, if the intrinsic noise signal can be reduced to 0.15 times the e-nose signal enose.t 2 /, then enose.
. D is defined as the signal amplification coefficient, and D D enose.t 2 /=enose.t 1 /. After simplification by x D ffiffiffiffiffiffiffiffiffiffi a=3b p , Equation (21) can be obtained: According to Equation (19), the limits of signal amplification coefficient D 1 in theory, which demonstrates that the enose signal can be tremendously amplified in practical measurement even if the intrinsic noise N .t/ is faced to a minor reduction. As a non-linear signal processing method, SR transfers intrinsic noise energy into the weak input signal. So the input weak signal is strengthened and the intrinsic noise is effectively reduced. For prawn samples with different freshness, e-nose feature measurement information is easily submerged into the interfering signals induced by the interactions between other gases and gas sensor array. E-nose feature information gets too weak to be presented in the experiments when using SR. DCSSR analysis relies on the effective combination of 2 or more SR procedures. In this study, 2 SR models are cascaded in serial successively. Intrinsic noise can be tremendously reduced and e-nose feature information can be effectively amplified via DCSSR method. 25 Experimental results demonstrate that DCSSR method is more effective than SR method in prawn freshness determination utilizing e-nose.

Conclusions
In this paper, penaeus orientolis prawn freshness rapid determination using e-nose and non-linear SR technique is proposed. The results are listed in the following: (a) E-nose responses to prawn samples stored at 4 C under different storage time are measured. Physical/chemical indexes (firmness, pH, TVB-N, TVC, and human sensory evaluation) are examined to provide freshness references for e-nose analysis.
(b) Firmness and sensory preference scores increases with the increase of prawn storage time. However, pH, TVB-N, and TVC indexes increases during experimental procedure. According to standard protocols, that prawns from day0 to day 2 are fresh and the rest samples are not fresh.
(c) E-nose measurement data is processed by PCA, Aquatic products contain rich nutrition, but they deteriorate easily. According to food quality examination standards in operation, the analysis procedure is fussy and time-consuming. We are carrying further investigations on applications of e-nose technique in food quality rapid analysis.


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Intake of Lutein-Rich Vegetables Is Associated with Higher Levels of Physical Activity

Levels of physical inactivity, a major contributor to burden of disease, are high in many countries. Some preliminary research suggests that circulating lutein concentrations are associated with high levels of physical activity (PA). We aimed to assess whether the intake of lutein-containing foods, including vegetables and eggs, is associated with levels of PA in two studies conducted in different countries. Dietary data and PA data collected from participants in two cross-sectional studies: the Maine-Syracuse Longitudinal Study (MSLS), conducted in Central New York, USA (n = 972), and the Observation of Cardiovascular Risk Factors in Luxembourg Study (ORISCAV-LUX) (n = 1331) were analyzed. Higher intakes of lutein containing foods, including green leafy vegetables, were associated with higher levels of PA in both study sites. Increasing the consumption of lutein-rich foods may have the potential to impact positively on levels of PA. This needs to be further explored in randomized controlled trials.

For the present study, we used data collected from two prominent studies with similar data on diet and PA: the Maine-Syracuse Longitudinal Study (MSLS) in the United States, and the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX). The objective of the study was to examine associations between dietary intake of lutein-rich foods and levels of PA in two study sites quite disparate in terms of dietary and PA habits. As a result of the preliminary evidence that lutein may cause an increase in PA, it was hypothesized that a higher intake of lutein-rich foods, namely vegetables, fruit and eggs, would be positively associated with PA levels in both study sites, thus providing additional evidence to support increasing dietary lutein intake as a strategy for increasing PA.

Subjects
The study utilized data from two cross-sectional studies, the MSLS and ORISCAV-LUX, and included 2303 individuals, 972 from MSLS and 1331 from ORISCAV-LUX. Further details related to the methods of sampling for both studies appear below and in numerous publications [26][27][28][29].

Participants in MSLS (USA)
The MSLS is a longitudinal, community-based study of aging, cardiovascular risk factors and cognitive functioning in adults, aged 23-98 years [28][29][30][31]. The MSLS was conducted in Syracuse, New York (NY), USA and its catchment area (Central NY). At initial recruitment (1975), the sole exclusions were institutionalized people, diagnosed alcoholism, and psychiatric disorders. The data for the present cross-sectional study were taken from subjects returning for the sixth study wave (2001)(2002)(2003)(2004)(2005)(2006) when dietary intake measures were first obtained. Beginning with a sample of 1049 individuals, participants were excluded from the present analysis for the following reasons: missing data on diet or PA (n = 35), acute stroke (n = 28), probable dementia (n = 8), undergoing hemo-dialysis (n = 5), inability to read English (n = 1), and alcohol abuse after baseline (n = 1), leaving 972 participants. The University of Maine Institutional Review Board approved this study and informed written consent was obtained from all participants.

Participants in ORISCAV-LUX (Luxembourg)
ORISCAV-LUX was a nationwide, cross-sectional study on the prevalence of cardiovascular risk factors among the adult population of Luxembourg, aged 18-69 years, conducted in 2007-2009. A comprehensive description of the ORISCAV-LUX survey design, sampling methods and sample have been published elsewhere [26,27]. A representative random sample of 1432 individuals, stratified by sex, age, and district of residence completed the recruitment procedure [26,27]. After data cleaning, a sample of 1331 subjects was available for analyses from the ORISCAV-LUX study. The study was approved by the National Research Ethics Committee and the National Commission for Private Data Protection, and all participants gave informed written consent.

Dietary Assessment
In the MSLS, dietary intake was assessed using the food frequency questionnaire (FFQ) component of the Nutrition and Health Questionnaire [32][33][34]. Its acceptable validity has been demonstrated by comparison with dietary recall, protein excretion, and total energy expenditure data [32]. The dietary component questions participants about their frequency of consumption of 37 foods and beverages, from six response options: "never", "seldom", "once a week", "2-4 times/week", "5-6 times/week" and "once or more/day". The median score within each response option was used to estimate intakes per week; for example, two to four times per week was estimated at three. Individual lutein-containing foods available for analyses were: eggs, total vegetables, fresh fruit, green vegetables, other cooked vegetables, salad/raw vegetables, peas, tomatoes, and carrots.
Portion or serving sizes were not stipulated; therefore total energy was estimated in the following manner. The mean number of times each food was consumed on a weekly and then daily basis was calculated for all foods. Individual foods were categorized into six major food groups (grains, fruits, vegetables, protein foods, dairy foods, and fats/sweets/other). Total energy was therefore estimated by summing the number of servings per day of all foods and beverages [35].
In ORISCAV-LUX, dietary intake was assessed using a validated, semi-quantified FFQ, which assessed the frequency of consumption of 134 items over the previous three months [36,37]. Participants were asked how frequently they consumed one standardized portion of each food, from six response options: "never or rarely", "1-3 times/month", "1-2 times/week", "3-5 times/week", "once/day", and "two or more times/day". Energy and nutrient intake data, including alcohol (g/day) and total energy intake (Kcal/day), were compiled. Individual lutein-containing foods available for analyses were: eggs, total vegetables, fresh fruit, citrus fruit, berries, kiwi fruit, pear/apple, banana, plum/grapes, green vegetables, salad/raw vegetables, other vegetables, peppers, tomatoes, and carrots [38].

Physical Activity Data
In the MSLS, PA was measured with the Nurses' Health Study (NHS) Activity Questionnaire [39]. This questionnaire is reported to be a valid measure of long-term PA levels [40]. Participants were asked, "during the past year, what was your average time per week spent at each of the following recreational activities?" Activities included walking or hiking outdoors, jogging (slower than 10 min/mile), running (10 min/mile or faster), bicycling, calisthenics aerobics, exercise machine use, tennis, squash, or racquetball, lap swimming, and other aerobic recreation. The range of possible values for each activity was 0 to 11 h. A MET value for each activity was assigned using the compendium of physical activities developed by Ainsworth et al. [41] and values used previously by the Nurses' Health Study investigators [39,40]. The MET values for each activity were multiplied by the number of hours spent at each activity to obtain MET-hours per week for each activity. The total MET-hours per week spent at leisure time PA was obtained by summing the MET-hours for each individual activity, and then multiplied by 60 to obtain MET-minutes per week.
In ORISCAV-LUX, PA was measured using the short format International Physical Activity Questionnaire (IPAQ) [41,42], designed and validated to measure PA in large populations. Mean weekly moderate and intense PA time (in minutes per week) was calculated by multiplying self-reported time spent engaging in each with the reported number of days per week in which these activities were undertaken. These values were summed to obtain total mean PA time per week (moderate plus intense).
Poor, intermediate, and ideal health levels for PA were calculated using the American Heart Association definitions [43], where poor equates to no PA, intermediate equates to between 1 and 149 min per week of moderate intensity activity (or 1-74 min per week of vigorous intensity activity), and ideal equates to at least 150 min per week of moderate intensity activity (or ě75 min per week of vigorous intensity activity). In terms of MET-minutes, 150 min per week of moderate intensity activity equates to 500 MET-minutes per week [44].

Lifestyle and Health Data
Participants in both studies underwent physical and anthropometric measurements and blood tests. Standardized protocols for data collection were used. Body weight, height, body mass index (BMI), waist circumference, and blood pressure (BP) measures were assessed as described previously for both studies [26][27][28][29][30]45]. Standard assay methods were employed [28,45] to obtain fasting plasma glucose, serum triglycerides, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, total cholesterol, and C-rective protein (CRP) (all mg/dL). All participants completed self-administered questionnaires to gain information on demographic and socioeconomic characteristics, including age, sex, education (years), and smoking (cigarettes/day).

Statistical Analysis
According to the type of variable (continuous or categorical), general linear modelling or Chi-square tests were used to compare demographic variables, dietary intakes, and other health-related variables in the two samples (n = 972 for MSLS, n = 1331 for ORISCAV-LUX), according to PA level (poor, intermediate, and ideal).
The same analyses were then performed for the MSLS and ORICAV-LUX samples. Multiple linear regression analyses were used to evaluate relations between fruit and vegetable consumption and PA in each study. Covariates used in the regression models were selected based on the following criteria: they had to correlate significantly with both PA and fruit/vegetable intake in both samples, and had to be theoretically meaningful. The following two multivariable regression models were used: ‚ Covariate set 1: adjusted for age, sex, education ‚ Covariate set 2: covariate set 1 variables, plus smoking, C-reactive protein, BMI, glucose, HDL-cholesterol, and total energy intake.
Binary logistic regression analyses were performed to calculate the odds associated with less than ideal PA levels (less than 150 min per week of moderate intensity activity), according to fruit and vegetable intake. Analyses were performed for total vegetables, total fruit, as well as for individual foods common to both studies: green leafy vegetables, salad, tomatoes, carrots, other vegetables, and eggs. The same two multivariate models were employed as above. In both models, the highest intake of fruit or vegetable intake was considered as the reference group. Results are expressed as odds ratios and 95% confidence intervals (ORs, 95% CIs).
All statistical analyses were performed with PASW for Windows version 21.0 software (formerly SPSS Statistics Inc., Chicago, IL, USA); p < 0.05 was considered statistically significant. Table 1 shows the demographic variables, dietary intakes, PA levels, and other health-related variables for MSLS and ORISCAV-LUX participants. A similar proportion of subjects from each study had poor PA levels (10%-11%), but a greater propotion in ORISCAV-LUX met ideal levels (67%) than in the MSLS (56%). Overall, weekly PA time was over two times higher in the Luxembourg sample than in the Central NY sample. In both study samples, those who had ideal PA levels also had significantly lower blood pressure, triglyceride and CRP levels, lower BMI, and higher HDL-cholesterol, compared to those who had poor PA levels. From a dietary perspective, participants with ideal PA levels also consumed significantly more fruit and vegetables than those with both poor or intermediate PA levels.

Fruit and Vegetable Consumption and Physical Activity in MSLS
As may be seen in Table 2, in the MSLS, total vegetable and total fruit consumption were positively associated with total PA time (fully extended model, both p < 0.001). Green leafy vegetables, salad, tomatoes, carrots, and "other vegetables" were all significantly and positively related to PA (fully extended model, all p < 0.05). Consuming less than five servings per day of vegetables was associated with significantly higher odds of achieving less than ideal PA levels (OR: 2.6, 95% CI: 1.1-6.4), compared to consuming at least five servings per day (data not shown). Participants who did not consume at least one serving of salad per week also had significantly higher odds of achieving less than ideal PA levels (OR: 2.7, 95% CI: 1.6-4.8), compared to those who consumed at least one serving per week (data not shown).
Intakes of meat, dairy foods, eggs, and alcohol were unrelated to PA, while total grain intake was inversely associated with PA (data not shown).

Fruit and Vegetable Consumption and Physical Activity in ORISCAV-LUX
In ORISCAL-LUX, total vegetable consumption was positively associated with total PA time (fully extended model, p < 0.01). Green leafy vegetables and salad were positively associated with PA (fully extended model, both p < 0.05). Participants who did not consume at least one serving of salad per week also had significantly higher odds of less than ideal PA levels (OR: 1.7, 95% CI: 1.2-2.4), compared to those who consumed more than this (data not shown). Total fruit intake was not related to PA, but plum/grape consumption was positively associated with PA (fully extended model, p < 0.05). Total daily intake of grains, meat, dairy foods, eggs, and alcohol were unrelated to PA.

Discussion
We have demonstrated significant associations between a higher intake of lutein-rich foods, namely vegetables, and in particular green leafy vegetables, and a higher level of PA, in two different study sites. It might be argued that this association exists because people who consume more vegetables are "health conscious" and therefore also choose to engage in PA rather than being due to any effect of an increased dietary intake of lutein. However, recent data from two randomized controlled trials that supplemented the diets of rats and humans with lutein demonstrated that lutein increased PA [25] and that increases in PA were linearly related to increases in plasma lutein concentrations [24], thus suggesting a causative relationship between the consumption of lutein-rich foods and increases in PA.
The present study is also consistent with other observational research in the US [18,19]. Using data from the Third National Health and Nutrition Examination Survey [18], higher serum lutein + zeaxanthin was significantly associated with being more physically active (and with having higher dietary lutein + zeaxanthin), consistent with another US study showing a significant correlation between PA and dietary lutein + zeaxanthin intakes [19]. Correlations have also been noted between lower levels of dietary lutein + zeaxanthin and other lifestyle factors including smoking and alcohol consumption [18,19]. Several studies have also reported associations between higher levels of inflammation and lower levels of serum lutein + zeaxanthin [18,46]. However, while food frequency questionnaires that are typically used in large dietary surveys only provide assessments of the sum of lutein and zeaxanthin intakes, the increases in PA and reductions in sedentary time that were observed in our preliminary study [24] were only correlated with changes in plasma lutein and not changes in plasma zeaxanthin, suggesting that effects on PA behavior are only related to changes in lutein status.
Mean PA time was considerably higher in the Luxembourg site than Central NY, equating to a difference of approximately 6.5 h per week. This is consistent with data showing that Europeans walk and cycle, over two and four times respectively, the number of kilometres per person per year than people in North America [47]. Higher rates of walking and cycling as a means of transport have also been associated with a higher percentage of adults meeting the recommended levels of PA, as well as lower rates of diabetes [48].
We have previously shown that overall cardiovascular health, generated from seven health metrics, including diet and PA, was higher at the Luxembourg site than at the Central NY site [49]. Our findings are therefore robust, given that similar relations between lutein-rich foods and PA have been observed in two study sites that vary in terms of diet and activity levels.
Increases in physical inactivity and obesity, and decreases in fruit and vegetable consumption have been observed in the United States between 1998 and 2010 [15]. Intakes of vegetables are lower than recommended in both the United States and Australia [16,17]. Our results suggest that increasing vegetable consumption to achieve current recommended intakes of five to six servings per day for men and women aged 19-50 years [16] (or 2.5 cups of fruit and vegetables per day [17]), and including green, leafy vegetables should continue to be strongly encouraged. Modest increases in dietary lutein intake could quite readily be achieved through such dietary manipulation, providing substantial increases in plasma lutein concentrations, and potentially impacting on chronic disease risk by increasing PA. For example, increasing egg consumption to one per day in older adults has been shown to significantly increase serum lutein concentrations without elevating serum lipids and lipoprotein cholesterol concentrations, over just a five-week period [50]. The intake of eggs was not related to PA in either study site, however egg consumption was relatively low (mean intake of 3.3 eggs/week in MSLS and 1.1 eggs/week in ORISCAV-LUX). The low intakes may explain why there was no relationship observed with PA.
It is widely recognized that lutein is able to cross the blood-brain barrier and accumulates in brain regions that are associated with behavior regulation [21], and it is possible that this might contribute to the observed relationships between higher lutein intakes/blood status and increased PA. Indeed, the diet of early man included a high intake of green vegetative matter [51] and would thus have been a high-lutein diet. If the lutein from this diet accumulated in brain regions and influenced behavior to increase PA then this should have assisted with hunting and/or gathering activities, which would in turn have provided a survival advantage. Thus, from a teleological perspective, it makes evolutionary sense that the intake of lutein might influence PA behavior. However, the precise mechanism(s) by which lutein intake may positively effect PA levels remains to be ellucidated. Nevertheless, the consistent finding of an association between a higher intake of foods rich in lutein and higher levels of PA, despite the difference in PA patterns in the two study sites evaluated in the present study, adds to the robustness of the finding. We were able to examine these relationships while controlling for a number of potential confounding variables, such as cardiovascular risk factors which were collected in both studies. Further, both FFQ's collected information about intakes for the same specific foods that were used in the analysis, enabling comparison between sites. Each questionnaire enabled intake frequencies (of the same foods) to be calculated in terms of servings per day and week. In addition, the same individual foods from both questionnaires were used to calculate the total intakes of each of the main food groups (compared in Table 1). Thus, the analysis of the dietary data was comparable across sites and, despite the differences in PA patterns across sites, in both cases higher PA levels were associated with a greater intake of foods rich in lutein.
A number of limitations should be acknowledged. We are unable to generalize beyond the two geographic study sites in Central NY, USA and Luxembourg to draw conclusions regarding the United States and Luxembourg. Our focus in this study is on the agreement in findings for two different sites in two geographically and culturally disparate environments. Diet and PA data were based on participant self-reporting and the same instruments were not used in both studies. The blood analyses were not performed in the same laboratory, albeit standardized procedures were used in both. Also, the absorption of lutein is variable and dependent to a large extent on the food matrix within which it is consumed. Thus, it would have been beneficial to be able to evaluate effects of differences in blood lutein status on PA, but unfortunately these data were not available. An association between the consumption of lutein-rich foods and PA cannot be interpreted as causative. However, the fact that two recent studies have shown that dietary lutein supplementation that results in increased circulating lutein levels, result in increased PA [25] with the magnitude of increase in PA being linearly related to the magnitude of increase in plasma lutein concentration [24], does provide some support for the proposition that an increased dietary intake of lutein might cause an increase in PA.

Conclusions
There is an urgent need to develop innovative strategies to increase PA amongst people of all ages. Altering the diet to increase lutein-rich foods may present as a novel approach for inducing PA. Randomized controlled trials are required to determine whether increases in plasma lutein concentrations as a result of an increased dietary intake of lutein can increase PA. This may help inform as to whether increased dietary intake of lutein and associated heightened levels of PA mediate improvements in risk factors for major chronic diseases, including cardiometabolic disease, cancer, dementia, and depression.