Towards rapidly quantifying and visualizing starch content of sweet potato [Ipomoea batatas (L.) Lam] based on NIR spectral and image data fusion

The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000-4000 cm-1) and preprocessing (Standardization, Savitzky-Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90-180-270-360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models.

NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from 'Farm' to 'Table': Progresses, challenges, trends, and prospects

NIR sensors, in conjunction with advanced chemometric algorithms, have proven to be a powerful and efficient tool for intelligent quality evaluation of sweetpotato roots throughout the entire supply chain. By leveraging NIR data in different wavelength ranges, the physicochemical, nutritional and antioxidant compositions, as well as variety classification of sweetpotato roots during the different stages were adequately evaluated, and all findings involving quantitative and qualitative investigations from the beginning to the present were summarized and analyzed comprehensively. All chemometric algorithms including both linear and nonlinear employed in NIR analysis of sweetpotato roots were introduced in detail and their calibration performances in terms of regression and classification were assessed and discussed. The challenges and limitations of current NIR application in quality evaluation of sweetpotato roots are emphasized. The prospects and trends covering the ongoing advancements in software and hardware are suggested to support the sustainable and efficient sweetpotato processing and utilization.

Non-destructive detection of egg white and yolk morphology transformation and salt content of salted duck eggs in salting by hyperspectral imaging

Salt content is a crucial indicator of the maturity and internal quality of salted duck eggs (SDEs) during the pickling process. However, there is currently no valid and rapid method available for accurately detecting salt content. In the present study, we utilized hyperspectral imaging to no-destructively determine the salt content in egg yolks, egg whites, and whole eggs during the curing period. Firstly, principal component analysis was applied to explain the characteristics of egg yolk and white morphology transformation of SDEs with different maturities during curing. Secondly, sensitive spectral factors representative of changes in the salt content of SDEs were extracted by three spectral transformations (Savitzky-Golay SG, continuum removal CR, and first-order derivation FD) and three approaches of selecting characteristic wavelengths (successive projection algorithm SPA, uninformative variables elimination UVE and competitive adaptive reweighting sampling algorithm CARS). The results of the PLSR model suggested that the optimal models for predicting salt content in egg yolks, whites, and whole eggs were SG-UVE-PLSR (predicted coefficient of determination Rp2=0.912, predicted standard deviation SEp=0.151, residual prediction deviation RPD = 3.371), CR-CARS-PLSR (Rp2=0.873, SEp=0.862, RPD = 2.806), and CR-UVE-PLSR (Rp2=0.877, SEp=0.680, RPD = 2.851), respectively. Eventually, the optimal prediction model for the salt content of the whole egg was employed to a pixel spectral matrix to calculate the salt content values of pixel points on the hyperspectral image of SDEs. Additionally, pseudo-color techniques were employed to visualize the spatial distribution of predicted salt content. This work will provide a theoretical foundation for rapidly detecting maturity and enabling high-throughput quality sorting of SDEs.

A quality detection method of corn based on spectral technology and deep learning model

Corn is an important food crop in the world. With economic development and population growth, the nutritional quality of corn is of great significance to high-quality breeding, scientific cultivation and fine management. Aiming at the problems of cumbersome steps, time-consuming and laborious, and low accuracy in the current research on corn quality detection. This paper proposes to combine near-infrared (NIR) spectroscopy technology with deep learning technology to build a corn quality detection model based on convolutional neural network (LeNet-5). The original spectral data were preprocessed by wavelet transform (WT) and multivariate scattering correction (MSC) to remove noise interference and spectral scattering information. The Competitive Adaptive Reweighted Sampling Algorithm (CARS) was applied to optimize the characteristic wavenumber and reduce redundant data. According to the optimized characteristic wave number, it was input into the constructed corn quality detection model for simulation test, and the average detection accuracy rate of the test set was 96.46%, the average precision rate was 95.42%, the average recall rate was 97.92%, the average F1score was 96.64%, and the average recognition time was 51.95 s. Compared with traditional machine learning models such as BP neural network, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Naive Bayesian (NB), the deep learning LeNet-5 network model constructed in this paper has an average accuracy increase of 39.32%, and has a higher detection accuracy.