Development and Experiment of a Handheld Visible/Near Infrared Device for Nondestructive Determination of Fruit Sugar Content
FAN Shu-xiang1, WANG Qing-yan1, YANG Yu-sen2, LI Jiang-bo1, ZHANG Chi1, TIAN Xi1, HUANG Wen-qian1*
1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. Southwest Jiaotong University-University of Leeds Joint School, Chengdu 611731, China
Abstract：A handheld portable device for fruit sugar content was developed based on visible/near-infrared spectral analysis. The device consists of a micro-spectrometer, halogen lamps, OLED screen and microcontroller. The real-time analysis and control software of the microcontroller was written in C language with the help of the Keil 5 development tool. Combined with the spectrum acquisition program written by LabView, the spectra of fruit samples were collected by the developed device. Apples and big peaches were used to explore the detection accuracy of the device and the transfer of the model between two devices (master and slave). The visible-near infrared spectra of the apple and peach were collected in the spectral range of 600～950 nm under laboratory conditions and in the field. The spectral data of calibration set collected by the master device under laboratory conditions were preprocessed by smoothing, maximum normalization, second derivative and other preprocessing methods, followed by the sugar content models developed using partial least squares algorithm for apples and peaches respectively. The models were then imported to the custom software, making it possible for the master device to predict the sugar content of apples or peaches directly. The correlation coefficient and the root mean square error of the prediction set were 0.925, 0.587% and 0.821, 0.613% for apples and peaches, respectively. The models were transferred from the master device to the slave device by using the piecewise direct standardization (PDS) and canonical Correlation Analysis (CCA) algorithm. After comparison, it was found that better model transfer results were achieved based on the CCA algorithm. The correlation coefficient and root mean square error of the prediction set were 0.883, 0.641% and 0.805, 0.626% for apples and peaches, respectively. The model established under laboratory conditions was used to analyze the fruit spectral data collected on the tree, the correlation coefficient and root mean square error of the prediction set were 0.866, 0.741% and 0.816, 0.627% for apples and peaches, respectively. The results showed that the developed device had considerable potential to detect fruit sugar content under lab conditions, and in the field. With the help of the model transfer algorithm, the model can be shared and effectively transferred between different devices. The developed device could meet the demand for rapid, non-destructive, and on-site detection of internal fruit quality.
Key words：Nondestructive detection; Fruit; Visible-near infrared spectrum; Spectral analysis; Sugar content; Model transfer
樊书祥，王庆艳，杨雨森，李江波，张 驰，田 喜，黄文倩. 水果糖度可见-近红外光谱手持式检测装置开发与试验[J]. 光谱学与光谱分析, 2021, 41(10): 3058-3063.
FAN Shu-xiang, WANG Qing-yan, YANG Yu-sen, LI Jiang-bo, ZHANG Chi, TIAN Xi, HUANG Wen-qian. Development and Experiment of a Handheld Visible/Near Infrared Device for Nondestructive Determination of Fruit Sugar Content. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3058-3063.
[1] LIU Yan-de, XU Hai, SUN Xu-dong, et al（刘燕德, 徐 海, 孙旭东, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2020, 40(3): 922.
[2] Yan H, Siesler H W. NIR News, 2018, 29 (7): 8.
[3] Yang B, Guo W, Li W, et al. Journal of Food Process Engineering, 2019, 42(2): e12982.
[4] Guo W, Li W, Yang B, et al. Journal of Food Engineering, 2019, 257: 1.
[5] WANG Fan, LI Yong-yu, PENG Yan-kun, et al（王 凡，李永玉，彭彦昆，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2017, 33(19): 295.
[6] GUO Zhi-ming, CHEN Quan-sheng, ZHANG Bin, et al（郭志明，陈全胜，张 彬，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2017, 33(8): 253.
[7] Yu X, Lu Q, Gao H, et al. Journal of Near Infrared Spectroscopy, 2016, 24 (1): 69.
[8] ZHAO Juan, QUAN Peng-kun, ZHANG Meng-sheng, et al（赵 娟，全朋坤，张猛胜，等）. Transactions of The Chinese Society of Agricultural Machinery（农业机械学报）, 2019, 50(4): 326.
[9] Cavaco A M, Pires R, Antunes M D, et al. Postharvest Biology and Technology, 2018, 141: 86.
[10] Fan S, Wang Q, Tian X, et al. Biosystems Engineering, 2020, 193: 138.
[11] Workman J J. Applied Spectroscopy, 2018, 72(3): 340.
[12] LI Xin, BIN Jun, FAN Wei, et al（李 鑫，宾 俊，范 伟，等）. Chinese Journal of Analytical Chemistry（分析化学）, 2017, 45(7)：958.
[13] Donis-González I R, Valero C, Momin M A, et al. Agronomy, 2020, 10(1): 148.
