Nondestructive Identification of Chinese Chive Seeds and its Counterfeit Scallion Seeds Based on Machine Vision and Electronic Nose

IF 3.2 4区农林科学 Q2 FOOD SCIENCE & TECHNOLOGY Food Biophysics Pub Date : 2025-01-28 DOI:10.1007/s11483-025-09934-1

Qiang Zhang, Baomei Wu, Weizhong Liu

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Abstract

To explore a non-destructive identification method for distinguishing Chinese chive (Allium tuberosum Rottl. ex Spreng) seeds from their adulterant, scallion (Allium fistulosum L.) seeds, machine vision and electronic nose technologies were employed. Principal component analysis (PCA), linear discriminant analysis (LDA), artificial neural networks (ANNs), and random forest (RF) algorithms were utilized to perform discriminant analyses based on the acquired data. The comprehensive results indicated that the image-based discrimination method, which integrates PCA with LDA and RF, demonstrated excellent accuracy using the obtained image information. Notably, the RF model established using odor information from the electronic nose achieved the lowest error rates of 0.98% for the training set and 0.70% for the test set. Overall, it was found effective and feasible to apply pattern recognition technology, combining both image and odor information, for the discrimination between Chinese chive seeds and their adulterated scallion seeds.

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基于机器视觉和电子鼻的韭菜籽及其假冒葱花籽无损识别

探讨一种判别韭菜（Allium tuberosum Rottl）的无损鉴别方法。采用机器视觉和电子鼻技术，从掺假的葱种子中提取种子。利用主成分分析（PCA）、线性判别分析（LDA）、人工神经网络（ann）和随机森林（RF）算法对采集的数据进行判别分析。综合结果表明，将PCA与LDA和RF相结合的基于图像的识别方法对获得的图像信息具有良好的识别精度。值得注意的是，利用电子鼻气味信息建立的射频模型在训练集和测试集上的错误率最低，分别为0.98%和0.70%。综上所述，将图像和气味信息相结合的模式识别技术应用于韭菜籽和掺假葱籽的鉴别是有效可行的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Food Biophysics 工程技术-食品科技

CiteScore

5.80

自引率

3.30%

发文量

审稿时长

1 months

期刊介绍： Biophysical studies of foods and agricultural products involve research at the interface of chemistry, biology, and engineering, as well as the new interdisciplinary areas of materials science and nanotechnology. Such studies include but are certainly not limited to research in the following areas: the structure of food molecules, biopolymers, and biomaterials on the molecular, microscopic, and mesoscopic scales; the molecular basis of structure generation and maintenance in specific foods, feeds, food processing operations, and agricultural products; the mechanisms of microbial growth, death and antimicrobial action; structure/function relationships in food and agricultural biopolymers; novel biophysical techniques (spectroscopic, microscopic, thermal, rheological, etc.) for structural and dynamical characterization of food and agricultural materials and products; the properties of amorphous biomaterials and their influence on chemical reaction rate, microbial growth, or sensory properties; and molecular mechanisms of taste and smell. A hallmark of such research is a dependence on various methods of instrumental analysis that provide information on the molecular level, on various physical and chemical theories used to understand the interrelations among biological molecules, and an attempt to relate macroscopic chemical and physical properties and biological functions to the molecular structure and microscopic organization of the biological material.