Rapid and accurate detection of protein and starch contents is important to ensure maize quality. However, existing methods for rapidly detecting protein and starch contents often suffer from drying samples to improve accuracy but significantly increase the overall detection time. To bridge this research gap, this study aims to integrate spectral and image features of visible near-infrared hyperspectral imaging for rapid determination of protein and starch contents with high accuracy under the effect of water on maize kernels. First, the spectral information at 399.75–1005.80 nm, color (the first, second, and third-order moments of H, S, and V color channels), and texture (contrast, correlation, energy, homogeneity, and entropy) features data were extracted. Then, 40 prediction models, including various preprocessing methods and modeling algorithms, partial least squares regression (PLSR), support vector regression (SVR), and extreme learning machine (ELM), were established and compared. Furthermore, the characteristic wavelengths are selected by successive projection algorithm (SPA) and uninformative variables elimination (UVE), and further fused with color and texture features data to improve model accuracy. The results showed that the best prediction models of both protein and starch contents were built based on fusion data. The standard normal transformation (SNV)-SPA-Color-ELM-Protein model and multiplicative scatter correction (MSC)-UVE-Texture-ELM-Starch model were optimal, which can achieve rapid and accurate detection in maize kernels under the effect of water. 

Integration of spectral and image features of hyperspectral imaging for quantitative determination of protein and starch contents in maize kernels

Amylose content (AC) is critical in evaluating starch properties, significantly influencing the food industry and human nutrition. Although amylose is not completely linear, its unique structure makes it a key research focus across various scientific fields. Understanding amylose's structural and functional properties is essential for its applications in medical, nutritional, and industrial sectors. Accurate determination of AC, from simple qualitative assessments to precise quantitative measurements, is vital for effectively processing and using starch-rich products. The choice of AC determination method depends on the specific application and the required accuracy and detail. This review summarizes amylose's structural and functional characteristics and recent advancements in qualitative and quantitative AC determination techniques. It also provides insights into future trends and prospects for these technologies, emphasizing the need for more rapid, convenient, accurate, and customizable methods. In conclusion, advancements in amylose determination should enhance accuracy, speed, and ease of use to improve quality control and applications across various sectors while expanding our understanding of amylose and its functionalities. 

Evaluation of amylose content: Structural and functional properties, analytical techniques, and future prospects

Advanced chemometrics toward robust spectral analysis for fruit quality evaluation

In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Existing deep learning models for detecting citrus diseases and nutritional deficiencies grapple with issues related to recognition accuracy, complex backgrounds, occlusions, and the need for lightweight architecture. In response, we developed an improved YOLOv8-GABNet model designed specifically for citrus disease and nutritional deficiency detection, which effectively addresses these challenges. This model incorporates several key enhancements: A lightweight ADown subsampled convolutional block is utilized to reduce both the model’s parameter count and its computational demands, replacing the traditional convolutional module. Additionally, a weighted Bidirectional Feature Pyramid Network (BiFPN) supersedes the original feature fusion network, enhancing the model’s ability to manage complex backgrounds and achieve multiscale feature extraction and integration. Furthermore, we introduced important features through the Global to Local Spatial Aggregation module (GLSA), focusing on crucial image details to enhance both the accuracy and robustness of the model. This study processed the collected images, resulting in a dataset of 1102 images. Using LabelImg, bounding boxes were applied to annotate leaves affected by diseases. The dataset was constructed to include three types of citrus diseases—anthracnose, canker, and yellow vein disease—as well as two types of nutritional deficiencies, namely magnesium deficiency and manganese deficiency. This dataset was expanded to 9918 images through data augmentation and was used for experimental validation. The results show that, compared to the original YOLOv8, our YOLOv8-GABNet model reduces the parameter count by 43.6% and increases the mean Average Precision (mAP50) by 4.3%. Moreover, the model size was reduced from 50.1 MB to 30.2 MB, facilitating deployment on mobile devices. When compared with mainstream models like YOLOv5s, Faster R-CNN, SSD, YOLOv9t, and YOLOv10n, the YOLOv8-GABNet model demonstrates superior performance in terms of size and accuracy, offering an optimal balance between performance, size, and speed. This study confirms that the model effectively identifies the common diseases and nutritional deficiencies of citrus from Conghua’s “Citrus Planet”. Future deployment to mobile devices will provide farmers with instant and precise support. 

YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies

The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review

The rapid and accurate identification of citrus leaf diseases is crucial for the sustainable development of the citrus industry. Because citrus leaf disease samples are small, unevenly distributed, and difficult to collect, we redesigned the generator structure of FastGAN and added small batch standard deviations to the discriminator to produce an enhanced model called FastGAN2, which was used for generating citrus disease and nutritional deficiency (zinc and magnesium deficiency) images. The performance of the existing model degrades significantly when the training and test data exhibit large differences in appearance or originate from different regions. To solve this problem, we propose an EfficientNet-B5 network incorporating adaptive angular margin (Arcface) loss with the adversarial weight perturbation mechanism, and we call it EfficientNet-B5-pro. The FastGAN2 network can be trained using only 50 images. The Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) are improved by 31.8% and 59.86%, respectively, compared to the original FastGAN network; 8000 images were generated using the FastGAN2 network (2000 black star disease, 2000 canker disease, 2000 healthy, 2000 deficiency). Only images generated by the FastGAN2 network were used as the training set to train the ten classification networks. Real images, which were not used to train the FastGAN2 network, were used as the test set. The average accuracy rates of the ten classification networks exceeded 93%. The accuracy, precision, recall, and F1 scores achieved by EfficientNet-B5-pro were 97.04%, 97.32%, 96.96%, and 97.09%, respectively, and they were 2.26%, 1.19%, 1.98%, and 1.86% higher than those of EfficientNet-B5, respectively. The classification network model can be successfully trained using only the images generated by FastGAN2, and EfficientNet-B5-pro has good generalization and robustness. The method used in this study can be an effective tool for citrus disease and nutritional deficiency image classification using a small number of samples.

/pdf/citrus-disease-image-generation-and-classification-based-on-1fk5rn2l.pdf

Citrus Disease Image Generation and Classification Based on Improved FastGAN and EfficientNet-B5

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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review

Citrus Disease Image Generation and Classification Based on Improved FastGAN and EfficientNet-B5