Design and test of spoilage sensing monitoring system for fruit

Authors

GUO Chuang, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
GUO Zhi-ming, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
SUN Li, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
SONG Ye, Jinan Fruit Research Institute, All China Federation of Supply & Marketing Cooperatives, Jinan, Shandong 250220, China
ZOU Xiao-bo, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China

Abstract

Objective: In order to solve the difficulty of multi-gas dynamic monitoring and early warning of fruit spoilage. Methods: Based on the gas sensor, the fruit spoilage sensor detection system was developed, and the gas sensor module, data acquisition module and other modules were designed. Developed inspection software and integrated inspection system.Taking apple as the verification object, the response difference and change law of the gas sensor before apple corruption were analyzed. Results: The linear discriminant analysis, k-nearest neighbor and back-propagation artificial neural network （BP-ANN） chemometric methods were used to establish the classification model of apple before spoilage. The recognition rate of BP-ANN was the highest, the training set and prediction set were 99.53% and 99.38% respectively. Synergy interval, genetic algorithm, simulated annealing, ant colony algorithm and competitive adaptive reweighted sampling （CARS） combined with partial least square （PLS） were used to screen characteristic variables to establish the prediction model of days before corruption. The CARS showed an optimal performance in predicting the days before corruption, to achieve Rp of 0.974. Conclusion: It shows that the fruit spoilage detection based on gas sensor technology is feasible, and it provides a reference for the research and development of fruit spoilage detection system.

Publication Date

9-28-2021

First Page

66

Last Page

72

DOI

10.13652/j.issn.1003-5788.2021.09.010

Recommended Citation

Chuang, GUO; Zhi-ming, GUO; Li, SUN; Ye, SONG; and Xiao-bo, ZOU (2021) "Design and test of spoilage sensing monitoring system for fruit," Food and Machinery: Vol. 37: Iss. 9, Article 10.
DOI: 10.13652/j.issn.1003-5788.2021.09.010
Available at: https://www.ifoodmm.cn/journal/vol37/iss9/10

References

[1] 郭志明, 郭闯, 王明明, 等. 果蔬品质安全近红外光谱无损检测研究进展[J]. 食品安全质量检测学报, 2019, 10（24）: 8 280-8 288.
[2] 张小蓉, 赵敏. 物联网视角下鲜活农产品流通问题及对策探析[J]. 山西农业科学, 2015, 43（12）: 1 693-1 696, 1 714.
[3] MASTELLO R B, CAPOBIANGO M, CHIN S T, et al. Identification ofodour-active compounds of pasteurised orange juice using multidimensional gas chromatography techniques[J]. Food Research International, 2015, 75: 281-288.
[4] LASEKAN O, HUSSEIN F K. Classification of different pineapple varieties grown in Malaysia based on volatile fingerprinting and sensory analysis[J]. BMC Chemistry, 2018, 12（1）: 140.
[5] 刘妍, 周新奇, 俞晓峰, 等. 无损检测技术在果蔬品质检测中的应用研究进展[J]. 浙江大学学报（农业与生命科学版）, 2020, 46（1）: 27-37.
[6] LORENTE D, ESCANDELL-MONTERO P, CUBERO S, et al. Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit[J]. Journal of Food Engineering, 2015, 163: 17-24.
[7] 张海辉, 田世杰, 马敏娟, 等. 考虑直径影响的苹果霉心病透射光谱修正及检测[J]. 农业机械学报, 2019, 50（1）: 313-320.
[8] 张棣, 殷勇, 于慧春, 等. 高光谱融合马氏距离的贮藏黄瓜腐败预警方法[J]. 核农学报, 2020, 34（12）: 2 749-2 755.
[9] 薛书凝, 殷勇, 于慧春, 等. 香蕉贮藏中腐败基准确定与高光谱信息表征及腐败预警模型构建[J]. 光谱学与光谱分析, 2020, 40（12）: 3 871-3 877.
[10] 张建锋, 何勇, 龚向阳, 等. 基于核磁共振成像技术的香梨褐变检测[J]. 农业机械学报, 2013, 44（12）: 169-173, 147.
[11] 黎新荣. 电子鼻在沃柑贮藏时间识别中的应用[J]. 南方农业学报, 2018, 49（9）: 1 827-1 832.
[12] WEI Xun, ZHANG Yu-chen, WU Di, et al. Rapid and non-destructive detection of decay in peach fruit at the cold environment using a self-developed handheld electronic-nose system[J]. Food Analytical Methods, 2018, 11（11）: 2 990-3 004.
[13] 徐赛, 陆华忠, 王亚娟, 等. 基于电子鼻与物理特征融合的猕猴桃贮藏时间识别方法[J]. 食品科技, 2016, 41（3）: 292-297.
[14] 徐静, 赵秀洁, 孙柯, 等. 基于电子鼻和乙醇传感器判别草莓新鲜度的研究[J]. 食品与机械, 2016, 32（5）: 117-121.
[15] 韩璐, 邢梦珂, 陈继昆, 等. 基于气敏传感器检测草莓腐烂的移动式设备研发[J]. 食品安全质量检测学报, 2019, 10（9）: 2 502-2 508.
[16] 范霞, 陈荣顺. 水蜜桃采后贮藏期间风味物质及质构特性的研究[J]. 食品科技, 2019, 44（4）: 30-35.
[17] 徐赛, 陆华忠, 周志艳, 等. 基于高光谱与电子鼻融合的番石榴机械损伤识别方法[J]. 农业机械学报, 2015, 46（7）: 214-219.
[18] 袁鸿飞, 胡馨木, 杨军林, 等. 基于FT-NIR和电子鼻的苹果水心病无损检测[J]. 食品科学, 2018, 39（16）: 306-310.
[19] 楚松峰, 赵凤霞, 方双, 等. 基于PCA-SVM的红枣缺陷识别方法[J]. 食品与机械, 2021, 37（1）: 156-160, 198.
[20] 郭志明, 黄文倩, 陈全胜, 等. 苹果腐心病的透射光谱在线检测系统设计及试验[J]. 农业工程学报, 2016, 32（6）: 283-288.
[21] GUO Zhi-ming, GUO Chuang, CHEN Quan-sheng, et al. Classification for Penicillium expansum spoilage and defect in apples by electronic nose combined with chemometrics[J]. Sensors, 2020, 20（7）: 2 130.
[22] SUN Qing, ZHANG Min, YANG Pei-qiang. Combination of LF-NMR and BP-ANN to monitor water states of typical fruits and vegetables during microwave vacuum drying[J]. LWT-Food Science and Technology, 2019, 116: 108548.
[23] 曹念念, 刘强, 彭菁, 等. 基于近红外光谱技术的黄桃脆片可溶性固形物和硬度定量检测方法[J]. 食品与机械, 2021, 37（3）: 51-57.
[24] 朱向荣, 李高阳, 苏东林, 等. 基于近红外光谱与组合间隔偏最小二乘法的稻米镉含量快速检测[J]. 食品与机械, 2015, 31（4）: 43-46, 50.
[25] KUTSANEDZIE F Y H, AGYEKUM AA, ANNAVARAM V, et al. Signal-enhanced SERS-sensors of CAR-PLS and GA-PLS coupled AgNPs for ochratoxin A and aflatoxin B1 detection[J]. Food Chemistry, 2020, 315: 126231.
[26] MUSHARAVATI F, HAMOUDA A S M. Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems[J]. Advances in Engineering Software, 2012, 45（1）: 80-90.
[27] KUTSANEDZIE F Y H, CHEN Quan-sheng, HASSAN M M, et al. Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution[J]. Food Chemistry, 2017, 240: 231-238.
[28] 江水泉, 孙通. 基于可见/近红外光谱和变量选择的脐橙可溶性固形物含量在线检测[J]. 食品与机械, 2020, 36（2）: 89-93.