| 光谱学与光谱分析 |
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| Detection of Adulteration in Milk Powder with Starch Near Infrared |
| WANG Ning-ning1, SHEN Bing-hui2, GUAN Jian-jun1, ZHAO Zhong-rui1, ZHU Ye-wei3, ZHANG Lu-da2, YAN Yan-lu4, ZHENG Yu-yan5, DONG Cheng-yu6, KANG Ding-ming1* |
1. College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
2. College of Science, China Agricultural University, Beijing 100193, China
3. Beijing Kaiyuan Shengshi Science﹠Technology Development Co., Ltd., Beijing 100081, China
4. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
5. Shenyang Agricultural University, Shenyang 110866, China
6. Liaoning Dongya Seed Limited Company，Shenyang 110164, China |
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Abstract Three China trademarks of milk powder called Mengniu, Yili, Wandashan were taken as testing samples. Each of them mixed varied amount of starch in different gradient, which were consisted of 32 adulterated milk powder samples mixed with starch, was taken as standard samples for constructing predicted model. To those 32 samples, the reflecting spectrum characteristics in middle wave of near infrared spectrum with Near Infrared Spectrum Analyzer (Micro NIR 1700) produced by JDSU Ltd. USA were collected for five repeats in five different days. The time span was nearly two months. Firstly, we build the model used the reflecting spectrum characteristics of those samples with biomimetic pattern recognition (BPR) arithmetic to do the qualitative analysis. The analysis included the reliability of testing result and stability of the model. When we took ninety percent as the evaluation threshold of testing result of CAR（Correct Acceptance Rate）and CRR (Correct Rejection Rate), the lowest starch content of adulterate milk powder in all tested samples which the tested result were bigger than that abovementioned threshold was designated CAR threshold (CAR-T) and CRR threshold (CRR-T). CAR means the correct rate of accepting a sample which is belong to itself, CRR means correct rate of refusing to accept a sample which is not belong to itself. The results were shown that, when we constructed a model based on the near infrared spectrum data from each of three China trademark milk powders, respectively, if we constructed a model with infrared spectrum data tested in a same day, both the CAR-T and CRR-T of adulterate starch content of a sample can reach 0.1% in predicting the remainder infrared spectrum data tested within a same day. The three China trademarks of milk powder had the same result. In addition, when we ignored the trademarks, put the spectrum data of adulterate milk powder samples mixed with the same content of starch of three China trademarks milk powder together to construct a model, the CAR-T of mixed starch content of a sample may reach 0.1%, the CRR-T can reach 1%, if the model construction and predicting were performed with near infrared spectrum data tested in a same day. However, the CAR-T can just stably reach up to 5% and the CRR-T have the same result, if the model construction and predicting were crossly performed with mixed near infrared spectrum data tested in different days. Furthermore, the correct recognizing threshold mixed starch of a sample can stably reach up to 1% and the CAR-T can reach 5%, if the model construction was based on near infrared spectrum data combined the previous four days to predict the output of the another day. On the other hand, we also engaged quantitative analysis to the starch content in milk power with two kinds of arithmetic (PLSR, LS-SVR). In contrast with the testing outputs, the reliability of both the CAR-T and CRR-T in qualitative analysis was further validated.
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Received: 2013-10-30
Accepted: 2014-05-25
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Corresponding Authors:
KANG Ding-ming
E-mail: kdm@pku.edu.cn
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