光谱学与光谱分析, 2023, 43 (4): 1218, 网络出版: 2023-05-03  

基于近红外反射光谱的番茄糖分快速检测模型研究

Research on the Rapid Detection Model of Tomato Sugar Based on Near-Infrared Reflectance Spectroscopy
作者单位
1 中国农业大学信息与电气工程学院, 北京 100083
2 农业农村部智慧养殖技术重点实验室, 北京 100083
3 北京联合大学智慧城市学院, 北京 100101
摘要
番茄是一种营养丰富且深受人们喜爱的果蔬, 在全球都得到了广泛的种植, 而我国番茄产销量稳居全球首位。 番茄不仅在人们的生活中扮演了一个重要的角色, 在工业生产中也占据了举足轻重的位置, 我国番茄的出口也在不断增加。 番茄的糖分、 酸度、 维生素C及可溶性固形物含量是反映番茄内部品质的重要评价指标, 而可溶性固形物含量是这些内部品质的总和, 能够较好地表征番茄的内部品质。 因此, 实现对番茄可溶性固形物含量的快速检测对番茄的工业生产和日常生活有着巨大的帮助。 基于化学原理的传统检测方法会对番茄样品产生不可逆的破坏, 且耗时耗力, 难以应对我国现代工业生产的需要。 因此, 寻求番茄内部品质的快速无损检测技术成为了亟待解决的问题。 近年来, 近红外光谱分析在多个领域得到了广泛的应用; 基于近红外光谱检测方法对反映番茄甜度的可溶性固形物含量进行了相关性建模和预测研究。 实验搭建了近红外光谱检测平台, 选择了255个不同成熟度和品种的番茄样本, 每个样本采集了光谱数据和可溶性固形物含量值。 研究对比了SNV, MSC, NOR和SG等光谱数据预处理方法, 并采用K-S算法划分建模校正集和验证集。 同时, 为提高检测可靠度和建模效率, 研究对比了CARS, RF, SPA和UVE等算法来进行数据降维。 结果表明, 采用SNV加二阶15点SG平滑组合的预处理与CARS波段选择相结合, 利用选出的54个波段建立的模型效果较好, 校正、 验证和交互验证相关系数R2分别达到了0.90, 0.89和0.91, 均方根误差RMSE分别为0.14, 0.15和0.14°Brix。 利用自行搭建的近红外光谱检测平台可较好地实现了番茄糖分的快速检测。
Abstract
Tomato is a kind of nutritious vegetable in the world. It is deeply loved by people and is widely grown worldwide, especially in China. Tomatoes not only play an important role in people’s lives but also play a pivotal role in our industrial production. The export of tomatoes in our country is also increasing. The sugar, acidity, vitamin C and solid soluble content of tomato are important evaluation indicators to reflect the internal quality of tomato, and the content of solid soluble content is the sum of these internal qualities, which can better characterize the internal quality of tomato, so the solid soluble content of tomato can be achieved. The rapid detection of solid content is of great help to our industrial production and daily life, and the traditional method will do an irreversible destructive analysis of tomato samples, which is time-consuming and labor-intensive. It is not easy to meet the needs of modern industrial production in my country. Therefore, the development of rapid non-destructive testing technology for internal tomato quality has become a problem to be solved. In recent years, near-infrared spectroscopy has been widely used in many fields with the advantages of being fast and non-destructive. In this paper, based on the near-infrared spectroscopy detection method, the correlation modeling and prediction of soluble solids content reflecting the sweetness of tomato were studied. A near-infrared spectroscopy detection platform was built in the experiment, and a total of 255 tomato samples of different maturity and varieties were selected, and spectral data and soluble solid value were collected for each sample. The research compares spectral data preprocessing methods such as SNV, MSC, NOR and SG and uses the K-S algorithm to divide the modeling calibration and validation sets. At the same time, to improve the detection reliability and modeling efficiency, the research and comparison of band selection algorithms such as CARS, RF, SPA and UVE are carried out for spectral data dimensionality reduction. In the experimental results, the preprocessing of SNV plus second-order 15-point SG smoothing combination combined with the selection of CARS bands, the model established by using the selected 54 bands has a better prediction effect, and the correlation coefficient R2 of correction, verification and cross-validation is up to 0.90, 0.89 and 0.91, the root mean square error RMSE was 0.14, 0.15 and 0.14°Brix, respectively. The results show that the self-built near-infrared spectroscopy detection platform can better realize the rapid detection of tomato sugar.
参考文献

[1] YAO Yu-chen, ZHAO Yun, ZHAO Shu-na, et al(姚宇晨, 赵 芸, 赵抒娜, 等). China Fruit & Vegetable(中国果菜), 2021, 41(6): 144.

[2] XIE Bo-jie, LIU Xiao-qi, ZHANG Yang, et al(颉博杰, 刘晓奇, 张 洋, 等). Journal of Gansu Agricultural University(甘肃农业大学学报), 2021, 56(1): 94.

[3] LIU Yan-de, RAO Yu, SUN Xu-dong, et al(刘燕德, 饶 宇, 孙旭东, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(12): 3910.

[4] JIN Dan, ZHANG Da-kui, WANG Shou-kai, et al(金 丹, 张大奎, 王守凯, 等). Guangdong Chemical Industry(广东化工), 2018, 45(3): 118.

[5] de Brito A A, Campos F, Nascimento A R, et al. Food Control, 2021, 126: 108068.

[6] Liang Pei-Shih, Haff Ronald P, Hua Sui-Sheng T, et al. Biosystems Engineering, 2018, 166: 161.

[7] LIU Yan-de, XU Hai, SUN Xu-dong, et al(刘燕德, 徐 海, 孙旭东, 等). Chinese Optics(中国光学), 2020, 13(3): 482.

[8] GAO Sheng, WANG Qiao-hua, LI Qing-xu, et al(高 升, 王巧华, 李庆旭, 等). Chinese Journal of Analytical Chemistry(分析化学), 2019, 47(6): 941.

[9] Cortés V, Cubero S, Aleixos N, et al. Postharvest Biology and Technology, 2017, 133: 113.

[10] SHENG Xiao-hui, LI Zi-wen, LI Zong-peng, et al(盛晓慧, 李子文, 李宗朋, 等). Food Science and Technology(食品科学与技术), 2019, 44(5): 307.

[11] LI Wei, LUO Hua-ping, SUO Yu-ting, et al(李 伟, 罗华平, 索玉婷, 等). Xinjiang Agricultural Mechanization(新疆农机化), 2019, (5): 20.

[12] ZENG Ming-fei, ZHU Yu-jie, FENG Guo-hong, et al(曾明飞, 朱玉杰, 冯国红, 等). Food and Fermentation Industries(食品与发酵工业), 2022, 48(20): 252.

[13] JIANG Hong, TANG Rong-nian, YE Lin-wei(姜 鸿, 唐荣年, 叶林蔚). Natural Science Journal of Hainan University(海南大学学报·自然科学版), 2020, 38(2): 166.

[14] CHEN Yu, QIU Zhi-jun, ZHANG Bin(陈 煜, 邱智军, 张 彬). Journal of Instrumental Analysis(分析测试学报), 2021, 40(12): 1690.

[15] ZHANG Xu-hui, ZHANG Kai-xin, ZHANG Chao, et al(张旭辉, 张楷鑫, 张 超, 等). Journal of Xi’an University of Science and Technology(西安科技大学学报), 2020, 40(5): 760.

[16] ZHANG Yi-zhuo, TU Wen-jun, LI Chao, et al(张怡卓, 涂文俊, 李 超, 等). Journal of Northeast Forestry University(东北林业大学学报), 2016, 44(10): 79.

[17] SA Ji-ming, JIANG He, XIE Kai-wen, et al(撒继铭, 江 河, 谢凯文, 等). Acta Optica Sinica(光学学报), 2021, 41(15): 235.

[18] ZHOU Meng-ran, YU Dao-yang, HU Feng, et al(周孟然, 余道洋, 胡 锋, 等). Journal of Henan Normal University(Natural Science Edition)[河南师范大学学报(自然科学版)], 2021, 49(2): 46.

崔天宇, 卢中领, 薛琳, 万诗颀, 赵可新, 王海华. 基于近红外反射光谱的番茄糖分快速检测模型研究[J]. 光谱学与光谱分析, 2023, 43(4): 1218. CUI Tian-yu, LU Zhong-ling, XUE Lin, WAN Shi-qi, ZHAO Ke-xin, WANG Hai-hua. Research on the Rapid Detection Model of Tomato Sugar Based on Near-Infrared Reflectance Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2023, 43(4): 1218.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!