Development of a model for intensity of flavour character in blackcurrant concentrates

The feasibility of routine quantification of flavour character intensity in fruit concentrates by chromatography was studied. A set of 37 volatile components, previously identified as flavour-active by gas chromatography-olfactometry, were quantified in 133 different blackcurrant concentrates from three seasons, by gas chromatography with flame ionisation detection. Sensory data on intensities of flavour character in these concentrates were obtained using ratio scaling. Partial least squares regression (PLS) was employed to develop models describing relationships between intensity of flavour character and concentrations of specific flavour components. Separate models were obtained for concentrates produced within three single seasons. Individual concentrates varied in: geographical origin; post-harvest fruit storage (chilled or frozen); and concentration strategy, either conventional thermal or freeze technology. Cross-validation (by PLS) determined the number of correlation factors required to reach minimum prediction error for each model. The final model had a regression coefficient of 0.80, dependent upon 10 flavour components, and was suited for predicting flavour character intensity in blackcurrant drinks from concentrates. © 1999 Society of Chemical Industry     

Detection of Blackcurrant Adulteration by Aronia Berry Using High Resolution Mass Spectrometry,Variable Selection and Combined PLS Regression Models

Aronia berry has a deep red colour, and as it is cheaper than blackcurrant, it may be used to adulterate blackcurrant juice and drinks made therefrom. The aim of the study was to produce a model using multivariate tools to quantify the level of adulteration of blackcurrant concentrates with aronia berry concentrates. Samples of various blends between the two fruits were analysed after dilution on an ultra high pressure liquid chromatography-time of flight mass spectrometry (UPLC-TOF-MS) to create partial least squares (PLS) models. Moreover, variable selection was investigated with the recursive weighted PLS (rPLS) method to increase PLS model quality, and different levels of fusion between data from the two ionisation modes were also compared. The best model was obtained from fusion of ionisation modes with variable selection using the rPLS method. The limit of detection of 5 % aronia berry concentrate in blackcurrant concentrate was achievable with ±1 % error on the adulteration level.     

Relationships of Overall Estery Aroma Character in Lagers with Volatile Headspace Congener Concentrations

In lager beers the intensity of “estery” aroma character is regarded as an important component of sensory quality, but its origins are somewhat uncertain. Overall “estery” aroma intensity was predicted from capillary gas chromatographic (GC) data following solid phase micro extraction (SPME) of headspaces. Estery character was scored in 23 commercial lagers using rankrating, allowing assessors (13) constant access to a range of appropriate standards. From univariate data analysis, all assessors behaved similarly and lagers fell into three significantly different groups: low (1), high (1) and intermediate (21). The quantification of 36 flavour volatiles by SPME of headspaces was reproducible and principal component analysis explained 91% total variance. Multiple linear regression could utilise only a restricted (26) set of flavour volatiles, whereas partial least square regression, that considered all flavour components, showed significant differences and improved prediction. However, an artificial neural network that could compensate for non‐linearities and interactions in ester perception gave the most robust prediction at R² = 0.88.     

Using artificial neural networks to develop prediction models for sensory-instrumental relationships; an overview

The possibility of using neural networks for modelling instrumental-sensory relationships is investigated. The advantages and disadvantages of using artificial neural networks (ANNs) are considered and compared with those of the multivariate linear methods of principal components regression (PCR) and partial least squares regression (PLS). In particular the problem of modelling nonlinear relationships is considered. It is concluded that ANNs cannot replace PCR and PLS for linear relationships but do offer potential for modelling nonlinear relationships.     

Application of Artificial Neural Networks (ANNs) in Wine Technology

In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.     

Factors influencing quantities of sugars and organic acids in blackcurrant concentrates

 Use of endogenous non-volatile flavour components, i. e. sugars and organic acids, in fruit juice products is desirable. A study of 133 blackcurrant concentrates from three seasons examined variation in sugars and acids arising from storage of fruit at freezing or sub-ambient temperature, seasonal differences, geographical origin and choice of conventional thermal-evaporative or freeze concentration technology. Compared with freeze concentrates, conventional concentrates had significantly higher contents of total sugars and acids, notably malic acid, and higher fructose/glucose, lower malic/citric acid and similar sugar/acid ratios. Concentrates from frozen fruit generally had smaller amounts of fructose, total sugars and fructose/glucose ratios than those from fresh fruit, as well as less citric, ascorbic and total acids and lower sugar/acid ratios. Principal component analysis of 40 randomly chosen concentrates showed that variance is dominated by differences in fructose, total sugars and ascorbic acid contents and sugar/acid ratios. Geographical origin and concentration technology were major sources of variance but changes in post-production sub-ambient storage could not be excluded. Received: 4 July 1997 /  Revised version: 14 October 1997     

Compositional variations and sensory acceptability of apple juice drink

Apple juice drinks containing 60% of Granny Smith or Jonathan juice, 8–14% soluble solids and Brix: acids ratios of 15:1 to 30:1 were assessed by a panel of twenty-five tasters. Sweetness and sourness increased with increasing levels of soluble solids and acidity respectively. Sourness showed little change with soluble solids at constant Brix: acid ratio but sweetness changed significantly. Intensity of flavour increased with soluble solids but the changes differed in the two types of drinks. Drinks having different soluble solids or Brix:acid ratios sometimes had similar flavour acceptabilities. Sweetness or lack of sourness, and intensity of flavour appeared to account for much of the flavour acceptability of the drinks. Equations for predicting flavour acceptance, based on Brix and Brix:acid ratios, were also developed.     

Prediction of consumer liking from trained sensory panel information: Evaluation of neural networks

This study was set out to establish artificial neural networks (ANN) as an alternative to regression methods (multiple linear, principal component and partial least squares regression) to predict consumer liking from trained sensory panel data. The sensory profile and acceptability of 10 market samples of beef bouillon products were measured. The products were distinct as evaluated by the trained sensory panel. A total of 100 regular beef bouillon product users from Manila measured overall liking, flavour, aftertaste and mouthfeel of the products. Curve fitting method was applied to identify sensory drivers of consumer liking. The sensory drivers of consumer liking were used as explanatory variables in artificial neural networks and regression methods. To overcome the limitations of regression methods we have used artificial neural network techniques to model consumer liking score as a function of trained sensory panel scores and achieved quite encouraging results. Our simulation experiments show that though the regression methods such as multiple linear regression (MLR), principal component regression (PCR) and partial least square (PLS) give an accurate prediction of consumer liking scores, this approach is not robust enough to handle the variations normally encountered in trained sensory panel data. ANNs were trained using the sensory panel raw data and transformed data. The networks trained with sensory panel raw data achieved 98% correct learning, the testing was in a range of 28–35%. Suitable transformation method was applied to reduce the variations in trained sensory panel raw data. The networks trained with transformed sensory panel data achieved about 80–90% correct learning and 80–95% correct testing. It is shown that due to its excellent noise tolerance property and ability to predict more than one type of consumer liking using a single model, the ANN approach promises to be an effective modelling tool.     

Development and evaluation of multiple regression models for prediction of sweet cherry liking

In two experiments, multiple regression models were developed and evaluated to identify the relevant sensory attributes for cherry liking. In Experiment 1, 16 judges evaluated 18 cherry varieties for seven visual characteristics (colour intensity, uniformity-of-colour, speckles, size, stem length, external firmness and ‘visual’ liking) and seven flavour/texture characteristics (flesh firmness, flesh colour intensity, juiciness, sweetness, sourness, flavour intensity and ‘flavour/ texture’ liking). Stepwise multiple regression was used to develop the most appropriate statistical models for prediction of visual and flavour/texture liking based on visual and flavour/texture characteristics, respectively. Both models were simple and easily understandable with two sensory variables. The best model for visual liking required only size and uniformity-of-colour variables; whereas, the best model for flavour/texture liking required sweetness and flavour intensity variables. In Experiment 2, 18 judges evaluated 30 sweet cherry cultivars, using the same methodology, to create a validation data set. Correlation coefficients (R) and prediction standard errors (PSEs) between the observed (Experiment 2) and predicted (Experiment 1) liking scores were used to evaluate the prediction equations. The prediction equation for flavour/texture liking was most satisfactory (R = 0.85, PSE = 0.61). A new equation developed from the validation data confirmed the importance of sweetness and flavour intensity. In contrast, the prediction equation for visual liking was less satisfactory (R = 0.56) and a new equation developed from the validation data set confirmed only size as an important variable.     

Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content*

Thermal resistance of the fabrics is one of the decisive parameters in terms of comfort; however it can change due to wetting. Therefore, thermal resistance of wetted fabric is important for comfort performance of garments. In recent years, artificial neural networks (ANN) have been used in the textile field for classification, identification, prediction of properties and optimization problems. ANNs can predict the fabric thermal properties by considering the influence of all fabric parameters at the same time. In this study, ANNs were used to predict thermal resistance of wetted fabrics. For this aim, two different architectures were experienced and high regression coefficient (R²) between the predicted (training and testing) and observed thermal resistance values were obtained from both models. The obtained regression coefficient values were over 90% for both models. Then it can be said that ANNs could be used for predicting thermal resistance of wetted fabrics successfully.     

Predicting the organoleptic stability of beer from chemical data using multivariate analysis

Delaying flavour staling, to prolong the shelf life of beer, is one of the greatest challenges facing the brewing industry today. In this study, a series of lager beers were brewed using five European barley varieties and four chemical indicators of the degree of beer ageing were correlated with the sensory evaluation: E-2-nonenal, β-damascenone, 2-furaldehyde and 5-hydroxymethyl-2-furaldehyde (5-HMF). A statistical strategy using principal component analysis and multiple linear regression was applied to draw relationships between the sensory and chemical data sets. Additionally, the relative significance of each of the chemical data on the organoleptic stability of beer was evaluated within the method. 5-HMF was the only studied carbonyl compound whose concentration cannot be used for predicting the total taste score of beers. E-2-nonenal, in contrast, was found to be the most discriminant carbonyl compound under consideration for predicting the flavour stability of beer.     

Changes in the pleasantness of caffeine-associated flavours consumed at home

Previous laboratory studies have reported that moderate caffeine consumers develop a liking for the flavour of a novel caffeinated drink. The present study tested whether results from laboratory studies are applicable to real-life situations. Forty moderate caffeine consumers were randomly allocated to four conditions (n = 10). Three of these conditions involved consumption of two novel flavoured fruit drinks, one caffeinated (100 mg) and one without caffeine, at different times of the participants’ daily routine (breakfast, anytime, night). Participants in the final (control) condition evaluated the drinks on the three test days only. Those participants who received the drinks at breakfast showed a significant increase in liking for the flavour of the caffeinated drink, whereas those who consumed the drinks at night showed a significant decrease in liking for the same flavour. Results imply that the post-ingestive effect of caffeine may depend on the time and condition during which the caffeinated flavour is consumed.     

Boysenberry and blackcurrant drinks increased the plasma antioxidant capacity in an elderly population but had little effect on other markers of oxidative stress

BACKGROUND: The consumption of fruit and vegetables promotes good health by protecting against various degenerative diseases. Even though the constituents responsible are not known, some evidence indicates that the antioxidant properties of fruit and vegetable phytochemicals are responsible. Previous studies have shown that blackcurrant and Boysenberry reduce oxidative stress using in vitro cell systems. The aim of this study was to determine if blackcurrant or Boysenberry drinks could improve measures of oxidative stress and inflammation in an elderly population with below‐average memory abilities. The intervention parallel study was fully blinded with a placebo control. RESULTS: Of the six measures of oxidative stress assessed, only plasma antioxidant capacity significantly increased for both the Boysenberry and blackcurrant treatments compared with the placebo. Plasma malondialdehyde decreased in both the Boysenberry and blackcurrant treatments although the decrease was not statistically significant. Measures of oxidative stress for protein oxidation and lipid peroxidation improved for the berryfruit treatments during the study but were not statistically different from the placebo. CONCLUSION: Long‐term consumption of both the Boysenberry and blackcurrant drinks raised the plasma total antioxidant capacity of the study participants suggesting that Boysenberry and blackcurrant may help protect against oxidative stress‐related health conditions. Copyright © 2007 Society of Chemical Industry     

Classification of stevia sweeteners in soft drinks using liquid chromatography and time-of-flight mass spectrometry

The aim of this study was to develop a comprehensive analytical method for the characterisation of stevia sweeteners in soft drinks. By using LC and time-of-flight MS, we detected 30 steviol glycosides from nine stevia sweeteners. The mass spectral data of these compounds were applied to the analysis to determine steviol glycosides in nine soft drinks. On the basis of chromatographic data and principal-component analysis, these soft drinks were classified into three groups, and the soft drinks of each group, respectively, contained high-rebaudioside A extract, normal stevia extract or alfa-glucosyltransferase–treated stevia extract.     

浓缩果汁生产中嗜酸耐热菌的控制

嗜酸耐热菌对浓缩果汁加工成的饮料的风味影响较大，果汁饮料消费国对此项指标的要求越来越严．对果汁生产中嗜酸耐热菌的控制方法进行研究、探讨，有助于有效控制嗜酸耐热菌，提高果汁产品质量。     

Applications of artificial neural networks (ANNs) in food science

Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.     

Influence of pasture species on the flavour, odour and keeping quality of lamb and mutton

Lambs fed white clover species (Trifolium repens cv. Grasslands Huia or cv. Grasslands 4700) showed a stronger flavour in the fat and lean and a greater intensity of odour in the casseroled 12th rib chop than lambs fed on perennial ryegrass (Lolium perenne). The results for hoggets generally supported those for lambs. Differences in flavour of the lean of the lambs appeared within three weeks from the beginning of the experiment. Differences in intensity of flavour and odour between the shoulder, loin and leg of the hoggets were small. After storage for 8 months at —15°, the thiobarbituric acid (TBA) values of the lean and fat of the 12th rib chop were highly significantly greater in lambs fed white clover than in those fed perennial ryegrass. Similar differences were found in hoggets using carcasses that had been stored for one to two months. In the hoggets, significantly higher TBA values were found in the fatty tissues of the leg than in the loin and shoulder. Differences between the TBA values of the lean were not significant.