CAJ | 학술논문

脂肪酸含量是表征稻谷霉变信息的重要指标。为了解决传统化学分析法测定稻谷脂肪酸含量有损、费时、低效等问题，该文研究应用高光谱技术实施霉变稻谷脂肪酸含量无损检测的方法。研究选取人工制备的不同霉变时期的稻谷样本作为研究对象，利用高光谱仪结合理化试验方法测定其相应的光谱信息和脂肪酸含量，运用移动窗口平滑法（savitzky-golay, SG）和一阶微分（first derivation, FD）对光谱数据进行预处理，采用连续投影算法（successive projections algorithm, SPA）提取反映稻谷脂肪酸含量变化的光谱特征波段，应用回归分析法建立基于特征波段光谱反射值的稻谷脂肪酸含量预测模型，对比分析不同光谱预处理方法的模型预测效果。研究结果显示，原始光谱数据通过 SG 平滑和一阶微分处理后，分别经SPA方法优选出了14和10个光谱特征波段；采用SG-SPA-MLR（multivariable linear regression）方法构建的模型质量和稻谷脂肪酸含量预测效果均优于FD-SPA-MLR模型，校正时其内部交叉验证的相关系数RCV和均方根误差RMSECV分别为0.9419、11.9646 mg/(100 g)；预测时其外部验证的相关系数RP和均方根误差RMSEP分别为0.9366、12.3550 mg/(100 g)，模型对不同霉变时期的稻谷脂肪酸含量均具有较强的预测能力。研究表明，利用高光谱技术对稻谷脂肪酸含量实施无损检测具有可行性，可为将来快速检测稻谷霉变提供参考依据。
지방산함량시표정도곡매변신식적중요지표。위료해결전통화학분석법측정도곡지방산함량유손、비시、저효등문제，해문연구응용고광보기술실시매변도곡지방산함량무손검측적방법。연구선취인공제비적불동매변시기적도곡양본작위연구대상，이용고광보의결합이화시험방법측정기상응적광보신식화지방산함량，운용이동창구평활법（savitzky-golay, SG）화일계미분（first derivation, FD）대광보수거진행예처리，채용련속투영산법（successive projections algorithm, SPA）제취반영도곡지방산함량변화적광보특정파단，응용회귀분석법건립기우특정파단광보반사치적도곡지방산함량예측모형，대비분석불동광보예처리방법적모형예측효과。연구결과현시，원시광보수거통과 SG 평활화일계미분처리후，분별경SPA방법우선출료14화10개광보특정파단；채용SG-SPA-MLR（multivariable linear regression）방법구건적모형질량화도곡지방산함량예측효과균우우FD-SPA-MLR모형，교정시기내부교차험증적상관계수RCV화균방근오차RMSECV분별위0.9419、11.9646 mg/(100 g)；예측시기외부험증적상관계수RP화균방근오차RMSEP분별위0.9366、12.3550 mg/(100 g)，모형대불동매변시기적도곡지방산함량균구유교강적예측능력。연구표명，이용고광보기술대도곡지방산함량실시무손검측구유가행성，가위장래쾌속검측도곡매변제공삼고의거。
Riceis rich in starches, proteins and carbohydrates, and when it is polluted by fungus, it is easy to become decayed and hence produces some poisonous substances for human bodies. Once moldy rice goes into the circulation market, human health will suffer from serious hazard. Therefore, how to effectively detect fungus in rice has become a fundamental work of guarantying food security. At present, the detection of moldy rice mainly depends on artificial qualitative analysis, which means that detectors discriminate fungus in rice according to some physical indices such as color and aroma. The detection precision of the mentioned methods mostly depends on the knowledge or experience of operators and the indication of statistic tools chosen by operators, which will bring out artificial errors inevitably. The fatty acid content is an important indicator of fungus information in rice. In order to solve these problems presented in the traditional way such as destruction, time consuming and low efficiency, a non-destructive detecting method for fatty acid content in rice using high-spectral technologies was proposed in this paper. In the research, rice samples for 4 different storage periods by means of artificial cultivating were selected as study objects, and spectral information and fatty acid content were detected through high-spectral measurement and physical and chemical experiment. The spectral data obtained were preprocessed using the Savitzky-Golay (SG) smoothing and the first derivation (FD) method, and the characteristic spectrum that indicated the variations of fatty acid content was selected by the successive projections algorithm (SPA). The prediction model of fatty acid content in rice based on spectral reflectance was built by the regression analysis method, and the prediction effect was evaluated by comparing different preprocessed methods. Experimental results indicated that 14 and 10 spectral characteristic wavelengths, which were from the original spectral data after the SG smoothing and the FD preprocessing, were optimized and selected according to the SPA. The quality of modeling and prediction effect for fatty acid content in rice showed that the SG-SPA-MLR (multivariable linear regression) method was superior to the FD-SPA-MLR method. The correlation coefficient of cross-validation (Rcv) and the root mean square error of cross-validation (RMSECV) for the SG-SPA-MLR model were 0.9419 and 11.9646 mg/(100 g) respectively at the model correction stage, while the correlation coefficient of prediction (Rp) and the root mean square error of prediction (RMSEP) were 0.9366 and 12.3550 mg/(100 g) respectively at the stage of the model prediction. The optimal model showed a good prediction ability in fatty acid content of rice during different storage periods. In summary, the results have indicated that it is feasible to non-destructively predict fatty acid content variation in rice applying high-spectral technologies, and can be used as the reference for the rapid detection of fungus stress in rice in the future.