|
|
|
|
|
|
Research on Protein Powder Adulteration Detection Based on
Hyperspectral Technology |
LI Bin, YIN Hai, ZHANG Feng, CUI Hui-zhen, OUYANG Ai-guo* |
School of Intelligent Electromechanical Equipment Innovation Research Institute, East China Jiaotong University, Nanchang 330013, China
|
|
|
Abstract Protein powder is an essential nutritional supplement for bodybuilders, and the market demand is increasing. Some unscrupulous businessmen are adding cheap powder to protein powder for sale to profit. The traditional protein powder adulteration detection method is time-consuming, laborious, complicated and expensive. Hyperspectral technology has the advantages of easy operation and rapid detection without damaging the experimental sample. Therefore, this paper proposes the use of hyperspectral technology to achieve protein powder adulteration detection. In the experiments, three types of adulterants (corn flour, rice flour and wheat flour) with 5%~60% mass percentages and 5% concentration interval were added to the protein powder, and the spectral information of all samples was collected. In the qualitative discrimination of the three types of adulterants (corn flour, rice flour and wheat flour) in the protein powder, the spectral data were firstly processed using the pre-processing methods of convolutional smoothing (SG), normalization (Normalize), multiple scattering correction (MSC), baseline correction (Baseline) and standard normal transformation (SNV), and then the spectral data were established based on principal component regression ( PCR), backpropagation neural network (BPNN), and random forest (RF) models, among which the RF model built under the MSC preprocessing method based on full-band spectra is the best, and its overall accuracy reaches 100%. Its corresponding RP and RMSEP are 0.997 9 and 0.018 9, respectively. In the quantitative analysis of different adulterant concentrations in protein powder, the spectra of the three types of adulterated samples were pretreated with SG, Normalize, MSC, Baseline and SNV, respectively, and LSSVM models were established. The performance between the models under different pretreatment methods was compared. The best LSSVM prediction models were used for corn flour, rice flour and wheat flour adulterated in protein powder preprocessing methods were None, Baseline and Normalize, and then, the continuous projection algorithm (SPA) and competitive adaptive reweighting algorithm (CARS) were used to screen them and build LSSVM models. The RP corresponding to the SPA-LSSVM models for the three types of adulterated samples were 0.989 0, 0.986 0 and 0.997 9, and the RP of the CARS- LSSVM model corresponds to RP of 0.991 0, 0.994 6 and 0.999 1, so the CARS-LSSVM model for the three types of adulterated samples has a better prediction. Research shows that hyperspectral technology can achieve qualitative and quantitative detection of protein powder adulteration and simple operation, rapid and non-destructive detection.
|
Received: 2021-07-07
Accepted: 2021-10-22
|
|
Corresponding Authors:
OUYANG Ai-guo
E-mail: ouyang1968711@163.com
|
|
[1] HU Jun, XU Zhen, LI Mao-peng, et al(胡 军,徐 振,李茂鹏,等). Laser & Optoelectronics Progress(激光与光电子学进展),2020, 57(22): 370.
[2] GUAN Da-wei(官大威). A Dictionary of Forensic Medicine(法医学辞典). Beijing: Chemical Industry Press(北京:化学工业出版社), 2009.
[3] XU Xia,WU Xiao-tian, XU Jia-yu, et al(徐 霞, 吴笑天, 徐嘉钰,等). Journal of Nuclear Agricultural Sciences(核农学报),2020, 34(7): 1525.
[4] WANG Wei, YIN Yan-chun, LU Hong(王 巍,银燕春,卢 红). World Latest Medicine Information(世界最新医学信息文摘), 2018, 18(98): 123.
[5] ZHANG Tao(张 涛). China Health Industry(中国卫生产业), 2015, 12(9): 2.
[6] Huang M, Kim M S, Delwiche S R, et al. Journal of Food Engineering, 2016, 181: 10.
[7] LI Bin, LONG Yuan, LIU Huan, et al(李 斌,龙 园,刘 欢,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(2): 1.
[8] Ropodi A I, Pavlidis D E, Mohareb F, et al. Food Research International, 2015, 67: 12.
[9] Wu D, Shi H, He Y, et al. Journal of Food Engineering, 2013, 119(3): 680.
[10] SUN Jun, JIN Xia-ming, MAO Han-ping, et al(孙 俊,金夏明,毛罕平,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(21): 301.
[11] SUN Zong-bao, WANG Tian-zhen, LI Jun-kui, et al(孙宗保,王天真,李君奎,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(7): 2208.
[12] Zhao X, Wang W, Ni X Z, et al. Applied Sciences-Basel, 2018, 8(7): 1076.
[13] Xiong Z, Sun D W, Xie A, et al. Food Chemistry, 2015, 178(339):45.
[14] REN Zhi-shang, PENG Hui-hui, HE Zhuang-zhuang, et al(任志尚,彭慧慧,贺壮壮,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(S2): 466.
[15] XU Hong-mei, WEN Jiang, ZHONG Wen-jie, et al(徐红梅,文 江,钟文杰,等). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2017, 38(3): 295.
|
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[5] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[6] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[7] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[8] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[9] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[10] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[11] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[12] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
[13] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[14] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[15] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
|
|
|
|