Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data

Abstract

The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other’s drawbacks, synergistically contributing to an excellent result.

Sweet Chestnut (Castanea sativa Mill.) Nutritional and Phenolic Composition Interactions with Chestnut Flavor Physiology

“…Moreover, this technique has been widely used for the quality assessment of nuts. For example, a study conducted in 2021 [ 19 ] with sensory and qualitative analysis of chestnuts, used data fusion strategies, taking advantage of the synergistic effect of the information obtained from sensory analysis of chestnuts with the help of the panel and QDA test and the data obtained by NIR. This study showed that sweet chestnuts, due to their characteristics, have the potential to be used in secondary products such as jam and flour, as mentioned before.…”

“…On the other hand, it is a rich source of starch (carbohydrates), protein, dietary fiber, vitamins, minerals (such as potassium, phosphorous, and magnesium), lipids, and nutrients. Furthermore, it is a good source of antioxidants such as L -ascorbic acid, carotenoids, and phenolic compounds such as gallic and ellagic acids [ 2 , 15 , 16 , 17 , 18 , 19 ]. Due to the mentioned characteristics, the chestnut may have beneficial health effects, arousing consumer interest.…”

“…Due to the mentioned characteristics, the chestnut may have beneficial health effects, arousing consumer interest. Consumer awareness will require the development of reliable methods to select high-quality foods, both nutritionally and sensorily [ 19 ]. The nut’s nutritional value depends on its chemical composition, and this is the result of the interaction of the cultivar (genotype), environmental conditions (climatic factors, temperature, and radiation), and production practices (soil mineral composition, nutrient availability, diseases, and pest) [ 16 , 17 , 20 , 21 ].…”

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

“… [ 80 ] Environmental Evaluate the state of conversion over time for an ecosystem Conductivity; pH; NIR; Fluorescence emission-excitation data MLDF MCR-ALS PARAFAC; MCR-ALS / MLDF > individual model / [ 184 ] Quantify potentially toxic elements from soil NIR; TXRF LLDF; MLDF SVM UF; GA DF-individual model comparison response dependent; GA > UV e.d. [ 113 ] Food Chestnut cultivar identification Sensory evaluation; FT-NIR LLDF PLS-DA - DF-individual model comparison LLDF > individual model (response dependent) / [ 185 ] Authentication of raw and cooked free-dried rainbow trout fillets NIR; Colorimetry; Texture analysis LLDF PLS-DA; LDA; QDA; kNN - DF-individual model comparison LLDF > individual model e.d. [ 186 ] …”

Storage of the Early Ripe Almonds under Modified Atmosphere to Preserve Kernel Qualitative and Sensory Traits

“…The nut and kernel trait measurements were carried out using a digital caliper (Sigma-Aldrich, Germany) with an accuracy of 0.01 mm. The geometric mean diameter (Dg), sphericity (Φ) and volume (V) were calculated by using the following equations [25][26][27][28]: Dg = (LWT) 1/3 (1)…”