Visualization of Sugar Content Distribution of White Strawberry by Near-Infrared Hyperspectral Imaging

Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method's superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data.

Strawberry (Fragaria x Ananassa) intake on human health and disease outcomes: a comprehensive literature review

Strawberries provide a number of potential health promoting phytonutrients to include phenolics, polyphenols, fiber, micronutrients and vitamins. The objective of this review is to provide a comprehensive summary of recent human studies pertaining to the intake of strawberry and strawberry phytonutrients on human health. A literature search conducted through PubMed and Cochrane databases consolidated studies focusing on the effects of strawberry intake on human health. Articles were reviewed considering pre-determined inclusion and exclusion criteria, including experimental or observational studies that focused on health outcomes, and utilized whole strawberries or freeze-dried strawberry powder (FDSP), published between 2000-2023. Of the 60 articles included in this review, 47 were clinical trials, while 13 were observational studies. A majority of these studies reported on the influence of strawberry intake on cardiometabolic outcomes. Study designs included those examining the influence of strawberry intake during the postprandial period, short-term trials randomized with a control, or a single arm intake period controlling with a low polyphenolic diet or no strawberry intake. A smaller proportion of studies included in this review examined the influence of strawberry intake on additional outcomes of aging including bone and brain health, and cancer risk. Data support that the inclusion of strawberries into the diet can have positive impacts during the postprandial period, with daily intake improving outcomes of lipid metabolism and inflammation in those at increased cardiovascular risk.

Evaluating Soluble Solids in White Strawberries: A Comparative Analysis of Vis-NIR and NIR Spectroscopy

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible-near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500-978 nm) and NIR (908-1676 nm) data collected from 180 samples of "Tochigi iW1 go" white strawberries and 150 samples of "Tochigi i27 go" red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R2p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R2p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system.

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible-Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400-1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R2 and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

Research on hyperspectral regression method of soluble solids in green plum based on one-dimensional deep convolution network

Soluble solids content is an important evaluation index affecting the quality of greengage fruit. The SSC content of green plum determines the picking time of green plum and what products are finally made into the market, such as preserves or fruit wine. The traditional destructive experiment is not conducive to the subsequent processing of green plum, and the efficiency is low and the labor cost is high. In this paper, hyperspectral images of green plums are analyzed based on the DenseNet network model, and a sugar content prediction model for green plums is established. After experimental collection and screening, 366 samples were obtained for the prediction of sugar content. According to the ratio of 3:1, 274 samples were obtained for the training set and 92 samples for the test set. In the prediction of sugar content, compared with the PLSR and MobileNetV2 model, the Rp of the 1D-DenseNet121 model in this experiment increased by 8.95%, and 6.27% respectively. and the MAEp was reduced by 15.44% and 10.35% respectively. The 1D-DenseNet121 model had a faster iterative convergence rate than the MobileNetV2 model, showing better prediction performance, which is more in line with the actual demand for green plum sorting, effectively improving the low efficiency of traditional physical and chemical detection.

Successful Applications of NIR Spectroscopy and NIR Imaging in the Food Processing Chain

Forty years ago, Near InfraRed (NIR) was considered a sleeping technique among the spectroscopic ones [...].