Objective:In order to realize the detection of apple sugar content, a nondestructive prediction method of apple sugar content based on the characteristic gray series of apple reflection spot image in darkroom system is proposed. Methods:The laser with the peak wavelength of 670 nm absorbed by the apple was used as the illumination light source, which was incident from the illumination port of the integrating sphere. The apple sample was placed at the sample port of the integrating sphere, and the reflection spot of the apple sample was obtained at the measuring port of the integrating sphere. Through the image collected by mobile phone, the gray information of the reflection spot image of apple under the irradiation of this wavelength was studied. It was found that the gray distribution of the reflection spot image of apple with different sugar content was different. Using partial least squares (PLS) algorithm, for 90 samples of three apple species in the training set, taking the pixel frequency (i.e. characteristic Gray Series) with gray value between 90~110 in the outer ring area of the reflected spot image as the sugar content related component, the three apple species were modeled and predicted respectively, so as to realize the nondestructive and rapid measurement of apple sugar content. Results:The predictive correlation coefficients of three kinds of apples in the training set were 0.89, 0.84 and 0.94 respectively. Based on the designed three kinds of apple sugar content prediction model, another 60 samples of the three apple species are verified. The prediction correlation coefficients of the three corresponding kinds of apple sugar degree in the verification set can reach 0.70, 0.73 and 0.76 respectively. Conclusion:Compared with the method of using multi wavelength fusion to predict apple sugar content, using a single strong absorption wavelength and the characteristic gray series of apple reflection spot image in darkroom system can be used as the basis of apple sugar content prediction, which provides a new research idea for apple sugar content prediction.

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[1] 乔鑫,彭彦昆,王亚丽,等.手机联用的苹果糖度便携式检测装置设计与试验[J].农业机械学报,2020,51(S2):491-498.QIAO Xin,PENG Yan-kun,WANG Ya-li,et al.Design and test of portable detection device for apple sugar content combined with mobile phone[J].Journal of Agricultural Machinery,2020,51(S2):491-498.
[2] 樊书祥,王庆艳,杨雨森,等.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):3 058-3 063.FAN Shu-xiang,WANG Qing-yan,YANG Yu-sen,et al.Development and test of handheld detection device for fruit sugar content by visible near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2021,41(10):3 058-3 063.
[3] 刘昊辰.苹果缺陷和糖度的近红外光谱技术动态在线检测研究[D].南昌:华东交通大学,2021:21-33.LIU Hao-chen.Dynamic on-line detection of apple defects and sugar content by near infrared spectroscopy[D].Nanchang:East China Jiaotong University,2021:21-33.
[4] 彭发,王震,刘双喜,等.基于偏最小二乘法和深度学习的近红外糖度预测[J].吉林农业大学学报,2021,43(2):196-204.PENG Fa,WANG Zhen,LIU Shuang-xi,et al.Near infrared sugar content prediction based on partial least squares and deep learning[J].Journal of Jilin Agricultural University,2021,43(2):196-204.
[5] 乔正明,詹成.基于近红外光谱和SSA-ELM的苹果糖度预测[J].食品与机械,2021,37(9):121-126.QIAO Zheng-ming,ZHAN Cheng.Prediction of apple sugar content based on near infrared spectroscopy and ssa-elm[J].Food & Machinery,2021,37(9):121-126.
[6] 许丽佳,陈铭,王玉超,等.高光谱成像的猕猴桃糖度无损检测方法[J].光谱学与光谱分析,2021,41(7):2 188-2 195.XU Li-jia,CHEN Ming,WANG Yu-chao,et al.Nondestructive testing method for sugar content of kiwifruit by hyperspectral imaging[J].Spectroscopy and Spectral Analysis,2021,41(7):2 188-2 195.
[7] 余志远.哈密瓜糖度近红外光谱检测方法研究及便携式装置设计[D].石河子:石河子大学,2021:20-32.YU Zhi-yuan.Study on near infrared spectroscopy detection method of sugar degree of Hami melon and design of portable device[D].Shihezi:Shihezi University,2021:20-32.
[8] 刘燕德,张雨,姜小刚,等.不同贮藏期水蜜桃硬度及糖度的检测研究[J].光谱学与光谱分析,2021,41(1):243-249.LIU Yan-de,ZHANG Yu,JIANG Xiao-gang,et al.Study on the determination of hardness and sugar content of honey peach in different storage periods[J].Spectroscopy and Spectral Analysis,2021,41(1):243-249.
[9] 介邓飞,杨杰,彭雅欣,等.基于高光谱技术的柑橘不同部位糖度预测模型研究[J].食品与机械,2017,33(3):51-54.JIE Deng-fei,YANG Jie,PENG Ya-xin,et al.Study on sugar content prediction model of different parts of citrus based on hyperspectral technology[J].Food & Machinery,2017,33(3):51-54.
[10] 高升,王巧华.基于可见/近红外透射光谱技术的红提糖度和含水率无损检测[J].中国光学,2021,14(3):566-577.GAO Sheng,WANG Qiao-hua.Nondestructive detection of sugar content and moisture content of red extraction based on visible/near infrared transmission spectroscopy[J].China Optics,2021,14(3):566-577.
[11] 司守奎,孙兆亮.数学建模算法与应用[M].北京:国防工业出版社,2015:311.SI Shou-kui,SUN Zhao-liang.Mathematical modeling algorithm and application[M].Beijing:National Defense Industry Press,2015:311.

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