Application of near-infrared spectroscopy to predict the cooking times of aged common beans (Phaseolus vulgaris L.)

Near infrared spectroscopy for cooking time classification of cassava genotypes

Cooking time is a crucial determinant of culinary quality of cassava roots and incorporating it into the early stages of breeding selection is vital for breeders. This study aimed to assess the potential of near-infrared spectroscopy (NIRS) in classifying cassava genotypes based on their cooking times. Five cooking times (15, 20, 25, 30, and 40 minutes) were assessed and 888 genotypes evaluated over three crop seasons (2019/2020, 2020/2021, and 2021/2022). Fifteen roots from five plants per plot, featuring diameters ranging from 4 to 7 cm, were randomly chosen for cooking analysis and spectral data collection. Two root samples (15 slices each) per genotype were collected, with the first set aside for spectral data collection, processed, and placed in two petri dishes, while the second set was utilized for cooking assessment. Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high (R C a l 2 ranging from 0.72 to 0.99). Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reachedR C a l 2   = 0.86 andR V a l 2 = 0.84, with a Kappa value of 0.53. Overall, the models exhibited a robust fit for all cooking times, showcasing the significant potential of NIRs as a high-throughput phenotyping tool for classifying cassava genotypes based on cooking time.

Insight into pectin-cation-phytate theory of hardening in common bean varieties with different sensitivities to hard-to-cook

In this study, a detailed quantitative analysis of the mechanisms linked with pectin-cation-phytate hypothesis of hard-to-cook development (HTC) was evaluated to assess the plausibility of this hypothesis. Several common bean varieties with varying sensitivities to HTC were characterized for pectin, cell wall bound calcium and inositol hexaphosphate (InsP₆) content before and after ageing. Ageing resulted in a significant decrease in InsP₆ content (resulting in calcium release) in all varieties. Despite not significantly changing during ageing, the cell wall bound calcium content significantly increased in most aged bean varieties upon short cooking indicating enhanced internal cation migration during the early phase of cooking in contrast to during ageing and soaking. Among the parameters evaluated in this study, the relative changes in InsP₆ content significantly correlated with the change in cooking times as well as changes in cell wall bound calcium content. Results obtained in this study suggest that in some bean varieties, pectin-cation-phytate hypothesis is the predominant mechanism by which hardening occurs during storage while in other varieties, the role of other factors such as phenolic crosslinking as suggested in literature cannot be ruled out.

Application of multivariate data analysis for food quality investigations: An example-based review

These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods.

Thermal treatment of common beans (Phaseolus vulgaris L.): Factors determining cooking time and its consequences for sensory and nutritional quality

Over the past years, the shift toward plant-based foods has largely increased the global awareness of the nutritional importance of legumes (common beans (Phaseolus vulgaris L.) in particular) and their potential role in sustainable food systems. Nevertheless, the many benefits of bean consumption may not be realized in large parts of the world, since long cooking time (lack of convenience) limits their utilization. This review focuses on the current insights in the cooking behavior (cookability) of common beans and the variables that have a direct and/or indirect impact on cooking time. The review includes the various methods to evaluate textural changes and the effect of cooking on sensory attributes and nutritional quality of beans. In this review, it is revealed that the factors involved in cooking time of beans are diverse and complex and thus necessitate a careful consideration of the choice of (pre)processing conditions to conveniently achieve palatability while ensuring maximum nutrient retention in beans. In order to harness the full potential of beans, there is a need for a multisectoral collaboration between breeders, processors, and nutritionists.