Preference mapping: A case study

The application of preference mapping to a survey in which 119 children tested 11 frozen chicken nuggets was investigated. A cluster analysis undertaken on consumer data provided three clusters. Consumers in the first cluster (43% of the sample) did not have a marked preference for these 11 nuggets. For the two remaining clusters, preference mapping was conducted. A feature of this case was that the sensory space, analysed by Principal Component Analysis, involved more than two dimensions. The purpose of the study was to describe how to choose first, the regression models between sensory and consumer data and second, the axes on which to represent these regressions, using the Fisher test of nested models.     

Preference mapping of apple varieties in Europe

A consumer test carried out in 7 different European countries compared 3 standard apple varieties to 8 new ones. A total of 4290 consumers took part in the test. Data from this test was used to develop a preference map for apple. The preference map was constructed with 3 main dimensions (1 – sweetness, fruitiness, flowery attributes, 2 – acidity, firmness, 3 – juiciness and crispness). Consumers were segmented in 6 clusters according to their preferences. The 6 clusters were grouped into two main mega clusters A (68% of consumers) and B (32% of consumers). Megacluster A (Clusters 1, 2, 5 and 6) was characterized by preferring sweet apples. Clusters 2 and 5 (41% of consumers) liked sweet apples independently of their acidity and firmness and moderate positive values on dimension of juiciness and crispness. Cluster 1 (21% of consumers) had an optimal point in positive values of the sweetness dimension, moderate negative value for acidity and firmness and moderate positive value for juiciness and crispness. Cluster 6 (6% of consumers) besides preferring sweet varieties disliked acid-firm varieties. As to regard to megacluster B (Clusters 3 and 4) (32% of consumers), they preferred varieties that were acidic-firm and juiciy and crisp with values in the mid range of the sweetness dimension. In spite of the difficulties in translating preference dimensions into standard practical values for fruit quality and the fact of being a punctual measurement of consumer behaviour, this preference map could be of practical use of different actors on the fruit value chain like marketers and breeders.     

Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 µm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306–379 µg kg^–1) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470–555 µg kg^–1). Coefficients of determination (r ²) indicated an “approximate to good” level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r ² = 0.71–0.83), and a “good” discrimination between low and high DON contents in the PLS validation models (r ² = 0.58–0.63). A “limited to good” practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 µg kg^–1 DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.     

Preference, liking and wanting for beverages in children aged 9-14 years: Role of sourness perception, chemical composition and background variables

The purpose of the study was to examine the relationship between children’s liking or wanting perception of sourness and food behavioural data in two types of beverages. In total 239 children (9-14 years old) evaluated apple juice and fruit drink in a design with 4 different dry matter concentrations. Multiple ranking was used to determine preference and perception of sourness, and a 5-point facial rating scale was used to assess liking and wanting. Children filled in questionnaires and BMI were registered. Multiple ranking showed that children on average had a high preference for versions of beverages perceived as less sour (p = 0.05). A PCA on rating data (liking and wanting, respectively), segmented the children in 3-4 segments. A minor segment of children with high liking and wanting for the apple juice perceived as most sour was obvious. L-PLS regression revealed visually clear correlations between chemical measurements, liking, wanting and behavioural data.     

Projective mapping: A tool for sensory analysis and consumer research

Studies have indicated that profiling and (dis)similarity scaling yield different perceptual product maps. Conceptually, these two procedures are different. This paper looks at a third and alternative method of producing a two-dimensional, perceptual map utilizing a projective-type method whereby individual assessors themselves are required to place products on the space according to the similarities and differences they perceive. However, visual comparison of the final results provided by each assessor is difficult and, hence, generalized Procrustes analysis is applied to compare each assessor's map for similarity with the others. In this study it was found that the perceptual map derived from projective mapping was as similar to the map derived from profiling as from dissimilarity scaling. However, consistency over repeated trials was greater for projective mapping than for the other two methods. It is suggested that projective mapping could be a potentially useful technique for linking sensory analysis and consumer research data.     

A comparison of generalised procrustes analysis and multiple factor analysis for projective mapping data

Generalised procrustes analysis and multiple factor analysis are multivariate statistical methods that belong to the family of multiblock methods. Both methods are often used for analysis of data from projective mapping (a.k.a. Napping). In this study, generalised procrustes analysis and multiple factor analysis are compared for a number of simulated and real data sets. The type of data used in this study were (I) random data from Monte Carlo simulations; (II) constructed data that were manipulated according to some specific criteria; (III) real data from nine Napping experiments. Focus will be on similarities of the consensus solutions. In addition we considered interpretation of the RV coefficient and individual differences between assessors.     

Simultaneous determination of flavor enhancers in stock cube samples by using spectrophotometric data and multivariate calibration

Spectrophotometric data followed by a suitable treatment of chemometric analysis were used for the simultaneous determination of monosodium glutamate (MSG), guanosine 5′-monophosphate (GMP) and inosine 5′-monophosphate (IMP) in stock cube samples, without any previous extraction step. By this way, the overlapping of the absorption spectra was resolved using a PLS-1 model. The concentration for experimental calibration matrix were varied between 5.03–34.2 μg mL⁻¹ for IMP and GMP, and 448–1399 μg mL⁻¹ for MSG. The relative errors of prediction (REPCV %) were 1.8, 2.8 and 3.1 for IMP, GMP and MSG, respectively.     

葛根提取物中麦芽糊精的中红外快速定量分析

中药提取物因生产工艺、储存条件的要求,需在生产过程中添加一定量的麦芽糊精。目前国内外还没有标准方法测定中药提取物中麦芽糊精的含量。本研究旨在建立一种快速检测葛根提取物混合粉末中辅料麦芽糊精含量的中红外快速测定方法,为企业有效把握提取物质量提供技术支持。以不同麦芽糊精含量的葛根提取物混合粉末样品88份为实验材料,采集已知麦芽糊精含量样品的中红外光谱,对谱图进行基线校正和归一化预处理,通过偏最小二乘回归方法建立定量模型。结果表明:所建定量模型预测效果良好,预测模型稳定性强、预测精度高,预测模型的决定系数R2为0.9819,预测标准差为3.205。采用中红外光谱检测技术可以快速定量葛根提取物中麦芽糊精的含量,有效地监督提取物市场以次充好的混乱现象,为市场监督部门提供技术支持。     

Consumer preferences for fresh tomato at the European scale: a common segmentation on taste and firmness

Although tomato flavor has not been a major goal for breeders, nowadays it becomes important as it is a subject of consumer complaint. A better knowledge of tomato consumer preferences, at the European level, should provide the basis for improvement of fruit quality and for market segmentation. In the framework of a large European project, 806 consumers from 3 countries, The Netherlands, France, and Italy, were presented with a set of 16 varieties representing the diversity of fresh tomato offer in order to evaluate their preferences. In parallel, sensory profiles were constructed by expert panels in each country. Preference maps were then constructed in each country revealing the structure of consumer preferences and allowing identification of the most important characteristics. Then a global analysis revealed that preferences were quite homogeneous across countries. This study identified the overall flavor and firmness as the most important traits for improving tomato fruit quality. It showed that consumer preferences from different European countries, with different cultures and food practices, are segmented following similar patterns when projected onto a common referential plan. Moreover, the results clearly showed that diversification of taste and texture is required to satisfy all consumers' expectations as some consumers preferred firm tomatoes, while others preferred melting ones and were more or less demanding in terms of sweetness and flavor intensity. Detailed comparisons also showed the importance of the fruit appearance in consumer preference.     

Interval and composite interval mapping of somatic cell score,yield, and components of milk in dairy cattle

Single-marker, interval-mapping (IM) and composite interval mapping (CIM) were used to detect quantitative trait loci (QTL) controlling milk, fat and protein yields, and somatic cell score (SCS). A granddaughter design was used to combine molecular genetic information with predicted transmitting abilities (PTA) and estimated daughter yield deviations (DYD) from eight Dairy Bull DNA Repository Holstein families. Models that included and excluded weights accounting for the uncertainty of the response variable were evaluated in each trait, family and phenotype (DYD and PTA) combination. The genotypic information consisted of 174 microsatellite markers along 29 Bos taurus autosomes. The average number of informative markers per autosome was three and the number of informative sons per family and marker varied between 21 and 173. Within-family results from the least squares single-marker analyses were used in expectation-maximization likelihood IM and CIM implemented with QTL Cartographer. Different CIM model specifications, offering complementary control on the background QTL outside the interval under study, were evaluated. Permutation techniques were used to calculate the genome-wide threshold test statistic values based on 1,000 samples. Results from the DYD and PTA analyses were highly consistent across traits and families. The minor differences in the estimates from the models that accounted for or ignored the uncertainty of the DYD (variance) and PTA (inverse of reliability) may be associated to the elevated and consistent precision of the DYD and PTA among sons. The CIM model best supported by the data had 10 markers controlling for background effects. On autosome (BTA) three, a QTL at 32 cM influencing protein yield was located in family five and a QTL at 74 cM for fat yield was located in family eight. Two map positions associated with SCS were detected on BTA 21, one at 33 cM in family one and the other at 84 cM in family three. A QTL for protein yield was detected between 26 and 36 cM on BTA six, family six, and a QTL for milk yield was detected at 116 cM on BTA seven in family three. The IM and CIM approaches detected a QTL at 3 cM on BTA 14 influencing fat yield in family four. Two map positions on BTA 29 were associated with significant variation of milk (0 cM) and fat yield (14 cM) in family seven. These results suggest the presence of one QTL with pleiotropic effects on multiple traits or multiple QTL within the marker interval. Findings from this study could be used in subsequent fine-mapping work and applied to marker-assisted selection of dairy production and health traits.     

Annotation of unknown yeast ORFs by correlation analysis of microarray data and extensive literature searches

Changes in the expression of genes were used to elucidate the metabolic pathways and regulatory mechanisms that respond to environmental or genetic modifications. Results from previously published chemostat datasets were merged with novel data generated in the present study. ORFs displaying significant changes in expression that correlated with those of other ORFs were analysed using GO mapping tools and supplemented by literature information. The strategy developed was used to propose annotations for ORFs of unknown function. The following ORFs were assigned functions as a result of this study: YMR090w, YGL157w, YGR243w, YLR327c, YER121w, YFR017c, YGR067c, YKL187c, YGR236c (SPG1), YMR107w (SPG4), YMR206w, YER067w, YJL103c, YNL175C (NOP13) YJL200C, YDL070C (FMP16) and YGR173W.     

Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy

The objective of the present study was to evaluate the ability of near-infrared (NIR) spectroscopy to predict chemical compositions of Thai steamed pork sausages in relation to different types of sample presentation forms of NIR measurements (with and without plastic casing). NIR spectra of sausages were scanned to predict the chemical compositions, protein, fat, ash and carbohydrate non-destructively. NIR spectrum features of the sausage samples were strongly influenced by physical properties of the samples, such as the presence of plastic casing and inhomogeneous physical structure inside the samples, yielding significant baseline fluctuations. Thus, regression models were developed using partial least squares (PLS) regressions with two pretreatment methods, namely multiplicative scatter correction (MSC) and second derivative, which overcame the baseline problems. The prediction results suggest that the contents for the protein, fat and moisture can be estimated well with the proper selection of the pretreatment method.     

Comparison of PLS, PCR and MLR for the quantitative determination of foreign oils and fats in butter fats of several European countries by their triglyceride composition

Three methods for evaluation of gas Chromatographic data have been compared. Multiple linear regression (MLR) as developed by Precht, principal component regression (PCR) and partial leastsquares regression (PLS) have been applied to the detection of foreign fat added to pure butter fat samples obtained from several European countries. The emphasis was put on building a calibration model for the general detection of foreign fat and therefore only one was built for the quantitation of various vegetable oils and lard MLR, as developed for German butter fat, was found to be appropriate also for the detection of the addition of about 3–5% foreign fat depending on the formula used. PCR calibration leads to a model with 11 factors indicating a detection limit of about 3 % foreign fat added. PLS seems to offer the lowest detection-limits (of about 2%) of the methods compared.