基于多标记深度森林算法的冷鲜羊肉新鲜度无损检测方法

doi:10.3964/j.issn.1000-0593(2024)02-0580-08

Abstract
References
Related Citation (15)

Download: PDF (4460 KB)
Export: BibTeX | EndNote (RIS)

Abstract Mutton freshness is affected by many factors, and the detection is generally carried out based on sensory properties, physical and chemical products of decomposition, microbial reproduction and other aspects. However, the freshness detection of mutton based on a single indicator has great limitations and low applicability, and it is not easy to evaluate the mutton freshness comprehensively. Moreover, the traditional detection methods are complex and inefficient, which cannot meet the daily actual needs. As a fast, nondestructive and efficient intelligent detection technology, hyperspectral imaging technology can effectively collect the surface, internal composition and physical and chemical changes in the process of mutton putrefaction. This paper proposes an evaluation model for the freshness of chilled mutton based on the improved deep forest algorithm, which adds feature screening to mine the spectral information related to multiple evaluation indicators. It adds layer growth control to prevent the model from over fitting effectively. This paper collects 400~1 000 nm hyperspectral data of mutton samples stored at 4 ℃ for 0~14 days .Total volatile base nitrogen (TVB-N), pH, total aerobic plate count (TAC) and the approximate number of coliforms (ANC) indicator values are measured by laboratory methods. The representative spectra of mutton samples are extracted in the regions of interest. The original spectral data is preprocessed using the smoothing filtering and multivariate scattering correction methods. 18 spectral feature bands are extracted by using the continuous projection method, and the samples of the training set and the testing set are divided at a ratio of 3∶1. Establishment of the freshness classification model is used in the improved deep forest algorithm proposed in this paper. The results show that the overall accuracy of freshness classification is 0.985 7, and use Hamming loss, One-error, Ranking loss and Marco-AUC multi-label metrics to evaluate the performance of the model, which are 0.025 7, 0.014 3, 0.014 2 and 0.998 6 respectively. Theyare better than the traditional multi-label classification algorithm. The research shows that the multi-indicator freshness classification model can be used for rapid, nondestructive testing of mutton freshness. It improves the limitations of single-indicator model classification and provides a research method for multi-indicator nondestructive testing of subsequent hyperspectral imaging technology.

Key words：Hyperspectraltechnology; Chilledmutton; Freshness; Multi label classification; Deep forest

Received: 2022-07-17 Accepted: 2022-12-17

ZTFLH:

TS207.7

Corresponding Authors: JIANG Xin-hua E-mail: jiangxh@imau.edu.cn

Cite this article:

XU Zi-yang,JIANG Xin-hua,BAI Jie, et al. A Nondestructive Method for Freshness Detection of Chilled Mutton With Multiple Indicators and Improved Deep Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 580-587.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)02-0580-08 OR https://www.gpxygpfx.com/EN/Y2024/V44/I02/580

[1] ZHANG Yu, TIAN Hai-qing, WANG Ke, et al（张珏，田海清，王轲，等）. Journal of China Agricultural University(中国农业大学学报), 2020, 25(5): 100.
[2] Yang Ling, Wu Ting, Liu Yun, et al. Journal of Spectroscopy, 2018, 2018(10): 2314(doi: 10.1055/2018/2413874).
[3] Zhang Xueqi, Jiang Tao, Guo Na, et al. Current Microbiology, 2020, 77: 826.
[4] BAI Xue-bing, MA Dian-kun, ZHANG Meng-jie, et al（白雪冰，马殿坤，张梦杰，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2022, 42(7): 1993.
[5] LIU Yan, ZHOU Xin-qi, YU Xiao-feng, et al（刘妍，周新奇，俞晓峰，等）. Journal of Zhejiang University（Agriculture and Life Sciences）[浙江大学学报（农业与生命科学版)], 2020, 46(1): 11.
[6] ZHOU Quan，ZHU Da-zhou，WANG Cheng，et al（周全, 朱大洲, 王成, 等）. Food Science（食品科学）, 2010，31(23): 423.
[7] XIA Yang-yi，ZHAO Luan（夏杨毅，赵鸾）. Journal of Food Science and Biotechnology（食品与生物技术学报），2020，39(12): 6.
[8] Magwaza Lembe S, Opara Umezuruike Linus，Nieuwoudt H，et al. Food & Bioprocess Technology，2012，5(2): 425.
[9] Dellaporta Rick. Journal of Texture Studies, 2008, 39(4): 426.
[10] JIANG Sha，YAN Cai-xia，FAN Xin，et al（姜莎，闫彩霞，范鑫，等）. Meat Industry（肉类工业）, 2021，2: 43.
[11] WANG Hao-yun, SONG Jin, PAN Lei-qing, et al（王浩云，宋进，盘磊庆，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2020, 36(5): 302.
[12] Jiang Xinhua, Xue Heru, Zhang Lina, et al. Computers and Electronics in Agriculture, 2018, 155(1): 371.
[13] Zhang M L, Zhou Z H. IEEE Transactions on Knowledge & Data Engineering, 2014, 26(8): 1819.
[14] Wu Z H, Qin S W, Nie R C, et al. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(10): 5200.
[15] Guo Y M, Chung F L, Li G Z, et al. IEEE Access, 2019, 7(4): 103863.
[16] Pereira R B, Plastino A, Zadrozny B, et al. Artificial Intelligence Review, 2018, 49(1): 57.
[17] Zhang M L, Zhou Z H. Pattern Recognition, 2007, 40(7): 2038.
[18] Read J, Pfahringer B, Holmes G, et al. Machine Learning, 2011, 85: 333.
[19] Zhang M L, Zhou Z H. IEEE Transactions on Knowledge & Data Engineering, 2006, 18(10): 1338.
[20] Liu H, Li X, Zhang S. IEEE Transactions on Cybernetics, 2017, 47(2): 499.
[21] XU Jie, LIANG Jiu-zhen, WU Qin, et al（许洁，梁久祯，吴秦，等）. Computer Science（计算机科学）, 2016, 43(1): 35.
[22] Zhou Z H, Feng J. National Science Review，2019, 6(1): 74.
[23] Stojanova D, Ceci M, Appice A, et al. Data Mining and Knowledge Discovery, 2012, 25(2): 378.
[24] Kocev D, Vens C, Struyf J, et al. Pattern Recogn, 2013, 46(3): 817.
[25] National Health and Family Planning Commission of the People's Republic of China(国家卫生和计划生育委员会). GB/5009.228—2016 National Food Safety Standard Determination of Total Volatile Base Nitrogen in Food(GB/5009.228—2016 食品国家安全标准食品中挥发性盐基氮的测定), 2016.
[26] National Health and Family Planning Commission of the People's Republic of China(国家卫生和计划生育委员会). GB/5009.237—2016 National Food Safety Standard Determination of pH of Food(GB/5009.237—2016 食品国家安全标准食品pH值的测定), 2016.
[27] National Health and Family Planning Commission of the People's Republic of China(国家卫生和计划生育委员会). GB/4789.2—2022 National Food Safety Standard Food Microbiological Examination: Aerobic Plate Count(GB/4789.2—2022 食品国家安全标准食品微生物学检验菌落总数测定), 2022.
[28] National Health and Family Planning Commission of the People's Republic of China(国家卫生和计划生育委员会). GB/4789.3—2016 National Food Safety Standard Food Microbiological Examination: Approximate Number of Coliforms(GB/4789.3—2016 食品国家安全标准食品微生物学检验大肠杆菌计数), 2016.