A Sustainable Way to Determine the Water Content in Torreya grandis Kernels Based on Near-Infrared Spectroscopy

Jiankai Xiang, Yu Huang, Shihao Guan, Yuqian Shang, Liwei Bao, Xiaojie Yan, Muhammad Hassan, Lijun Xu () and Chao Zhao ()
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Jiankai Xiang: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Yu Huang: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Shihao Guan: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Yuqian Shang: College of Chemical and Material Engineering, Zhejiang A&F University, Hangzhou 311300, China
Liwei Bao: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Xiaojie Yan: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Muhammad Hassan: US-Pakistan Centre for Advanced Studies in Energy, National University of Science and Technology, Islamabad 44000, Pakistan
Lijun Xu: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Chao Zhao: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China

Sustainability, 2023, vol. 15, issue 16, 1-13

Abstract: Water content is an important parameter of Torreya grandis ( T. grandis ) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as spectroscopy is accomplished in a fast, accurate, nondestructive, and sustainable way. The aim of this study was to realize the rapid detection of the water content in T. grandis kernels using near-infrared spectroscopy. The water content of T. grandis kernels was measured by the traditional drying method. Meanwhile, the corresponding near-infrared spectra of these samples were collected. A quantitative water content model of T. grandis kernels was established using the full spectrum after 10 outlier samples were removed by the Mahalanobis distance method and concentration residual analysis. The results showed that the prediction model developed from the partial least squares regression (PLS) method after the spectra were pretreated by the standard normal variate transform (SNV) achieved optimal performance. The correlation coefficient of the calibration set (R 2 c) and the cross-validation set (R 2 cv) were 0.9879 and 0.9782, respectively, and the root mean square error of the calibration set (RMSEC) and the root mean square error of the cross-validation set (RMSECV) were 0.0029 and 0.0039, respectively. Thus, near-infrared spectroscopy is feasible for the rapid nondestructive detection of the water content in T. grandis seeds. Detecting the water content of agricultural and forestry products in such an environmentally friendly manner is conducive to the sustainable development of agriculture.

Keywords: T. grandis kernels; near-infrared spectroscopy; water content; partial least squares regression; sustainable development; green technology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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