Classification of rice based on storage time by using near infrared spectroscopy and chemometric methods

Miao, Xin; Miao, Ying; Tao, Shaohua; Liu, DengBiao; Chen, Zuwu; Wang, Jiemin; Huang, Weidong; Yu, Yaying

doi:10.1016/j.microc.2021.106841

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…The classification purpose of this work was achieved by both PLS-DA and SVM with 100% classification accuracy [79]. PLS-DA showed its efficiency in detecting the freshness of rice based on storage time using FT-NIR with an accuracy of 96%, whereas the application of KNN achieved an accuracy of 100% [136]. In relation to the analysis of rice by NIR, L.-H. Xie et al led a discrimination of two kinds of rice, waxy, which contains very low apparent amylose content, and non-waxy rice.…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

Kharbach,

Alaoui Mansouri,

Taabouz

et al. 2023

Foods

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In today’s era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

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Section: Infrared Spectroscopymentioning

confidence: 99%

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

Kharbach,

Alaoui Mansouri,

Taabouz

et al. 2023

Foods

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show abstract

“…After preprocessing and feature dimensionality reduction, the classification model accuracy was higher than that modeled using raw fluorescence hyperspectral data. This is because, by preprocessing, the spectral noise can be removed from the spectral curve as much as possible, highlighting the valuable information of the spectrum [31]. Then, after processing by feature dimensionality reduction, the spectral data dimensions are reduced to reduce further the influence of spectral noise, which reduces the amount of data and the influence of useless data [32].…”

Section: Modeling Analysis After Preprocessing and Feature Dimensiona...mentioning

confidence: 99%

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

Kang,

Fan,

Zhan

et al. 2024

Molecules

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A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475–1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.

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“…It is often not a good method for varietal and adulteration discrimination on large batches of honey. As one of the most rapidly developing high-tech analytical techniques in recent years, near-infrared spectroscopy (Lei et al, 2021;Miao et al, 2021) has been widely used in the quality inspection of agricultural products due to its rapidity (Wang et al, 2021;Yang et al, 2021), non-destructiveness and simplicity (Wu et al, 2016). Taking a variety of honeys sold on the market in Ya'an, Sichuan as the research object, the research on the identification of honey varieties and the adulteration of single honey varieties with different mass fractions of fructose syrup by near-infrared spectroscopy was discussed.…”

Section: Introductionmentioning

confidence: 99%

Honey quality detection based on near-infrared spectroscopy

et al. 2023

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As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.

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Classification of rice based on storage time by using near infrared spectroscopy and chemometric methods

Cited by 18 publications

References 30 publications

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

Honey quality detection based on near-infrared spectroscopy

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