Hu Jun, Shi Hongyang, Zhan Chaohui, Qiao Peng, He Yong, Liu Yande
School of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang 330013, China.
School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
Foods. 2022 Nov 3;11(21):3498. doi: 10.3390/foods11213498.
Walnuts have rich nutritional value and are favored by the majority of consumers. As walnuts are shelled nuts, they are prone to suffer from defects such as mildew during storage. The fullness and mildew of the fruit impose effects on the quality of the walnuts. Therefore, it is of great economic significance to carry out a study on the rapid, non-destructive detection of walnut quality.
Terahertz spectroscopy, with wavelengths between infrared and electromagnetic waves, has unique detection advantages. In this paper, the rapid and nondestructive detection of walnut mildew and fullness based on terahertz spectroscopy is carried out using the emerging terahertz transmission spectroscopy imaging technology. First, the normal walnuts and mildewed walnuts are identified and analyzed. At the same time, the image processing is carried out on the physical samples with different kernel sizes to calculate the fullness of the walnut kernels. The THz image of the walnuts is collected to extract the spectral information in different regions of interest. Four kinds of time domain signals in different regions of interest are extracted, and three qualitative discrimination models are established, including the support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) algorithms. In addition, in order to realize the visual expression of walnut fullness, the terahertz images of the walnut are segmented with a binarization threshold, and the walnut fullness is calculated by the proportion of the shell and kernel pixels.
In the frequency domain signal, the amplitude intensity from high to low is the mildew sample, walnut kernel, and walnut shell, and the distinction between walnut kernel, shell samples, and mildew samples is high. The overall identification accuracy of the aforementioned three models is 90.83%, 97.38%, and 97.87%, respectively. Among them, KNN has the best qualitative discrimination effect. In a single category, the recognition accuracy of the model for the walnut kernel, walnut shell, mildew sample, and reference group (background) reaches 94%, 100%, 97.43%, and 100%, respectively. The terahertz transmission images of the five categories of walnut samples with different kernel sizes are processed to visualize the detection of kernel fullness inside walnuts, and the errors are less than 5% compared to the actual fullness of walnuts.
This study illustrates that terahertz spectroscopy detection can achieve the detection of walnut mildew, and terahertz imaging technology can realize the visual expression and fullness calculation of walnut kernels. Terahertz spectroscopy and imaging provides a non-destructive detection method for walnut quality, which can provide a reference for the quality detection of other dried nuts with shells, thus having significant practical value.
核桃具有丰富的营养价值，深受广大消费者喜爱。由于核桃是带壳坚果，在储存过程中容易出现霉变等缺陷。果实的饱满度和霉变情况会影响核桃的品质。因此，开展核桃品质快速无损检测研究具有重要的经济意义。
太赫兹光谱位于红外和电磁波之间，具有独特的检测优势。本文利用新兴的太赫兹透射光谱成像技术对核桃霉变和饱满度进行快速无损检测。首先，对正常核桃和霉变核桃进行识别分析。同时，对不同果仁大小的实物样本进行图像处理，计算核桃仁的饱满度。采集核桃的太赫兹图像，提取不同感兴趣区域的光谱信息。提取不同感兴趣区域的四种时域信号，建立支持向量机（SVM）、随机森林（RF）和k近邻（KNN）算法三种定性判别模型。此外，为实现核桃饱满度的可视化表达，用二值化阈值对核桃的太赫兹图像进行分割，通过壳和果仁像素比例计算核桃饱满度。
在频域信号中，幅度强度从高到低依次为霉变样本、核桃仁、核桃壳，核桃仁、壳样本和霉变样本之间区分度较高。上述三种模型的总体识别准确率分别为90.83%、97.38%和97.87%。其中，KNN的定性判别效果最佳。在单一类别中，该模型对核桃仁、核桃壳、霉变样本和参考组（背景）的识别准确率分别达到94%、100%、97.43%和100%。对五类不同果仁大小的核桃样本的太赫兹透射图像进行处理，实现了核桃仁饱满度检测的可视化，与核桃实际饱满度相比误差小于5%。
本研究表明太赫兹光谱检测可实现核桃霉变检测，太赫兹成像技术可实现核桃仁饱满度的可视化表达和计算。太赫兹光谱和成像为核桃品质提供了一种无损检测方法，可为其他带壳干坚果的品质检测提供参考，具有重要的实际应用价值。
