Food and Fermentation Industries >

2020 , Vol. 46 >Issue 10: 264 - 270

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.023489

NIR hyperspectral imaging technology for rapid detection of acid value in fresh chilled pork

  • HE Hongju ,
  • WANG Wei ,
  • WANG Yangyang ,
  • MA Hanjun ,
  • CHEN Fusheng ,
  • ZHU Mingming ,
  • ZHAO Shengming ,
  • KANG Zhuangli
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  • 1(School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China)
    2(Postdoctoral Research Base, Henan Institute of Science and Technology, Xinxiang 453003, China)
    3(College of Grain, Oil and Food, Henan University of Technology, Zhengzhou 450001, China)

Received date: 2020-02-03

  Online published: 2020-06-17

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Abstract

The aim of this study was to detect acid value of pork stored at 0-4 ℃ by using near infrared (NIR) hyperspectral technique (900-1 700 nm) in a rapid and nondestructive way. The hyperspectral images of pork samples were collected, and the reflectance spectral information within the region of interest of the images was extracted. Then eight methods including moving average smoothing (MAS), Savitzky Golay convolution smoothing (SGCS), median filtering smoothing (MFS), Gaussian filter smoothing (GFS), normalization correction (NC), multiplicative scatter correction (MSC), baseline correction (BC) and standard normal variate (SNV) were applied to preprocess the raw extracted spectra. Partial least squares (PLS) algorithm was used to establish the model for predicting the acid value of pork. The results showed that the full-band PLS models based on the raw spectra and BC spectra had better performance in prediction of acid value, with rP of 0.824, RMSEP of 0.594 mg/g in RAW-PLS model and rP of 0.825, RMSEP of 0.587 mg/g in BC-PLS model. Regression coefficient method (RC) and successive projections algorithm (SPA) were used to select the optimal wavelengths for PLS model optimization. The results showed that the RAW-RC-PLS model built with 28 optimal wavelengths selected from raw spectra by RC method had best prediction performance, resulting in rP of 0.846 and RMSEP of 0.569 mg/g. The whole study indicated that it is potential to realize the rapid and nondestructive detection of acid value of pork by NIR hyperspectral imaging technology.

Key words： hyperspectral imaging technology; acid value; partial least squares; regression coefficient; successive projections algorithm

Cite this article

HE Hongju , WANG Wei , WANG Yangyang , MA Hanjun , CHEN Fusheng , ZHU Mingming , ZHAO Shengming , KANG Zhuangli . NIR hyperspectral imaging technology for rapid detection of acid value in fresh chilled pork[J]. Food and Fermentation Industries, 2020 , 46(10) : 264 -270 . DOI: 10.13995/j.cnki.11-1802/ts.023489

References

[1] 赵娟红. 基于蛋白质组学分析不同贮藏温度下猪肉的品质变化机制[D]. 长沙:中南林业科技大学, 2019.
[2] 何鸿举, 王魏, 王慧, 等. 基于近红外光谱技术的生鲜猪肉质量检测研究进展[J]. 食品工业科技, 2019, 40(10): 360-368.
[3] 李莹, 张伟敏, 黄海珠, 等. 国内外猪肉质量快速无损检测技术的研究进展[J]. 食品研究与开发, 2017, 38(20): 208-213.
[4] 齐亮, 赵婕, 赵茂程. 冷鲜猪肉的新鲜度无损检测技术现状及THz检测技术展望[J]. 食品与机械, 2016,32(9): 219-224.
[5] 张雨晴, 李颖, 白婷婷, 等. 大连市售鲅鱼干化学及微生物指标分析[J]. 食品研究与开发, 2019, 40(7): 137-143.
[6] 黄琪评. 基于光谱成像技术的猪肉品质检测研究[D]. 镇江: 江苏大学, 2016.
[7] 张海云, 彭彦昆, 王伟, 等. 生鲜猪肉主要品质参数无损在线检测系统[J]. 农业机械学报, 2013, 44(4): 146-151.
[8] 王文秀, 彭彦昆, 徐田锋, 等. 双波段光谱融合的猪肉多品质参数同时检测方法研究[J]. 光谱学与光谱分析, 2016, 36(12): 4 001-4 005.
[9] SCHMUTZLER M, BEGANOVIC A, BÖHLER G, et al. Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis[J]. Food Control, 2015, 57: 258-267.
[10] 孙宏伟, 彭彦昆, 林琬. 便携式生鲜猪肉多品质参数同时检测装置研发[J]. 农业工程学报, 2015, 31(20): 268-273.
[11] 刘媛媛, 彭彦昆, 王文秀, 等. 基于偏最小二乘投影的可见/近红外光谱猪肉综合品质分类[J]. 农业工程学报, 2014, 30(23): 306-313.
[12] 刘芳, 王超, 杨菊, 等. 油脂酸价和过氧化值检测方法的研究进展[J]. 食品安全质量检测学报, 2019, 10(14): 4 478-4 482.
[13] 白亚斌, 刘友华, 丁崇毅, 等. 基于高光谱技术的牛肉-猪肉掺假检测[J]. 海南师范大学学报(自然科学版), 2015, 28(3): 270-273.
[14] HE H, SUN D, WU D. Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis[J]. Food Research International, 2014, 62: 476-483.
[15] 刘友华, 白亚斌, 邱祝福, 等. 基于高光谱图像技术和波长选择方法的羊肉掺假检测方法研究[J]. 海南师范大学学报(自然科学版), 2015, 28(3): 265-269.
[16] HE H, WU D, SUN D. Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging[J]. Innovative Food Science & Emerging Technologies, 2013, 18: 237-245.
[17] ZHAO H, FENG Y, CHEN W, et al. Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging[J]. Meat Science, 2019, 151: 75-81.
[18] BALAGE J M, SILVAS D L E, GOMIDE C A, et al. Predicting pork quality using Vis/NIR spectroscopy[J]. Meat Science, 2015, 108: 37-43.
[19] BARBIN D F, VALOUS N A, SUN D. Tenderness prediction in porcine longissimus dorsi muscles using instrumental measurements along with NIR hyperspectral and computer vision imagery[J]. Innovative Food Science & Emerging Technologies, 2013, 20: 335-342.
[20] LI W, LIN M, ZHANG Y. Application of wavelet transform and neural network in near infrared spectroscopy analysis in pork[C]. Harbin: Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2016.
[21] 林岩, 郭培源, 王昕琨. 基于近红外光谱的猪肉蛋白质及脂肪含量检测[J]. 食品科技, 2014, 39(2): 262-266.
[22] HUANG Q, CHEN Q, LI H, et al. Non-destructively sensing pork’s freshness indicator using near infrared multispectral imaging technique[J]. Journal of Food Engineering, 2015, 154: 69-75.
[23] MA J, SUN D, NICOLAI B, et al. Comparison of spectral properties of three hyperspectral imaging (HSI) sensors in evaluating main chemical compositions of cured pork[J]. Journal of Food Engineering, 2019, 261: 100-108.
[24] 王慧, 何鸿举, 张海曼, 等. 高光谱成像技术快速预测冷鲜鸡胸肉的嫩度[J]. 海南师范大学学报(自然科学版), 2018, 31(2): 164-170.
[25] CHENG W, SØRENSEN K M, ENGELSEN S B, et al. Lipid oxidation degree of pork meat during frozen storage investigated by near-infrared hyperspectral imaging: Effect of ice crystal growth and distribution[J]. Journal of Food Engineering, 2019, 263: 311-319.
[26] 张雷蕾, 李永玉, 彭彦昆, 等. 基于高光谱成像技术的猪肉新鲜度评价[J]. 农业工程学报, 2012, 28(7): 254-259.
[27] 何鸿举,王玉玲,乔红,等. 基于长波近红外光谱快速无接触评估小麦籽粒含水率[J]. 海南师范大学学报(自然科学版),2019,32(1):26-32.
[28] SHAN J, WANG X, RUSSEL M, et al. Comparisons of fish morphology for fresh and frozen-thawed crucian carp quality assessment by hyperspectral imaging technology[J]. Food Analytical Methods, 2018, 11(6): 1 701-1 710.
[29] JIANG H, WANG W, ZHUANG H, et al. Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef[J]. Food Analytical Methods, 2019, 12(10): 2 205-2 215.
[30] 何鸿举,王玉玲,乔红,等. NIR光谱法快速预测小麦籽粒干物质含量[J]. 海南师范大学学报(自然科学版),2019,32(1):33-38.
[31] 王文秀, 彭彦昆, 刘媛媛. 基于近红外光谱的猪肉新鲜度无损检测方法的改进[J]. 食品安全质量检测学报, 2015, 6(8): 3 007-3 013.
[32] WIEDEMAIR V, DE BIASIO M, LEITNER R, et al. Application of design of experiment for detection of meat fraud with a portable near-infrared spectrometer[J]. Current Analytical Chemistry, 2018, 14(1): 58-67.
[33] 张世芝, 胡树青, 张明锦. 基于回归系数的变量筛选方法用于近红外光谱分析[J]. 计算机与应用化学, 2012, 29(2): 227-230.
[34] 孙旭东, 郝勇, 蔡丽君, 等. 基于抽取和连续投影算法的可见近红外光谱变量筛选[J]. 光谱学与光谱分析, 2011, 31(9): 2 399-2 402.
[35] HE H, WU D, SUN D. Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets[J]. Journal of Food Engineering, 2014, 126: 156-164.
[36] HE H, WU D, SUN D. Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis–NIR) hyperspectral imaging[J]. Food Chemistry, 2014, 156: 394-401.
[37] LÒPEZ-MAESTRESALAS A, INSAUSTI K, JAREN C, et al. Detection of minced lamb and beef fraud using NIR spectroscopy[J]. Food Control, 2019, 98: 465-473.
[38] 汪希伟. 基于光谱成像的猪肉新鲜度检测方法[D]. 南京: 南京林业大学, 2014.
[39] HUANG H, LIU L, NGADI M O. Quantitative evaluation of pork marbling score along Longissimus thoracis using NIR images of rib end[J]. Biosystems Engineering, 2017, 164: 147-156.
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