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| 基于近红外光谱构建虹鳟鱼营养成分快速检测模型 |
| Construction of a rapid detection model for nutrient components of Rainbow trout based near-infrared spectroscopy |
| 投稿时间：2025-04-09 修订日期：2025-06-11 |
| DOI： |
| 中文关键词: 近红外光谱技术 虹鳟鱼 营养成分 快速检测 |
| 英文关键词:Near-infrared spectroscopy Rainbow trout Nutritional components Rapid detection |
| 基金项目:青年拔尖人才项目(2023TSYCCX066)；新疆维吾尔自治区重点研发任务专项(2022B02006-3)；新疆维吾尔自治区公益性科研院所基本科研业务经费资助项目(KY2024159) |
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| 中文摘要: |
| 目的 基于近红外光谱构建虹鳟鱼营养成分快速检测模型。方法 先采集200条虹鳟鱼近红外光谱数据，再用国标方法测定每条虹鳟鱼3种营养成分（水分、脂肪和蛋白质）的含量，将近红外光谱数据与营养成分数据一一对应，利用近红外光谱技术(near-infrared spectroscopy, NIR)结合偏最小二乘法(partial least squares, PLS)建立近红外快速检测模型，并筛选出最佳检测模型。结果 水分含量检测模型的预处理方法为多元散射校正(multiple scatter correction, MSC)，波段为4000~10000 cm-1时，模型最优；脂肪含量检测模型预处理方法为标准正态变量变换(standard normal variable transformation, SNV)，波段为5000~7144,7404~10000 cm-1时模型最优；蛋白质检测模型的预处理方法为二阶导数(ds2)+SNV+Savitzky-Golay平滑(sg9)，波段为4100~5100,5400~9000 cm-1时模型最优，最优模型的Q值、校正模型相关系数(RC)、交互验证集相关系数(RP)均较大，校正集标准差(SEC)和预测集标准误差(SCP)互相接近，满足最佳建模原则。结论 使用未参与建模的预测集对最佳模型进行验证，跌出结果为预测值与真实值（国标方法测定值）的绝对偏差均不超过5.7%，说明该模型可用于虹鳟鱼3种营养成分的检测，可实现虹鳟鱼营养成分的无损、快速检测，节约检测成本，缩短检测周期。 |
| 英文摘要: |
| Objective To establish a rapid detection model for the nutritional components of rainbow trout based on near-infrared spectroscopy. Methods Firstly, near-infrared spectral data of 200 rainbow trout were collected. Then, the contents of three nutritional components (moisture, fat and protein) of each rainbow trout were determined by national standard methods. The near-infrared spectral data and nutritional component data were matched one by one. The near-infrared rapid detection model was established by combining near-infrared spectroscopy (NIR) with partial least squares (PLS), and the best detection model was screened out. Results The pretreatment method of the moisture content detection model was multiple scattering correction (MSC), and the optimal model was obtained when the wavelength range was 4000–10000 cm–1. The pretreatment method of the fat content detection model was standard normal variable (SNV), and the optimal model was obtained when the wavelength range was 5000–7144 and 7404–10000 cm–1. The pretreatment method of the protein detection model was second derivative (ds2)+SNV+Savitzky-Golay smoothing (sg9), and the optimal model was obtained when the wavelength range was 4100–5100 and 5400–9000 cm–1. The Q value, the correlation coefficient of the correlation coefficients (RC), and the correlation coefficient of prediction (RP) of the optimal model were all relatively large, and the standard deviation of square error corrected (SEC) and standard error of cross validation (SCP) were close to each other, which met the best modeling principle. Conclusion The best model is verified by the prediction set that do not participate in the modeling. The absolute deviation between the predicted value and the true value (determined by the national standard method) is no more than 5.7%. This indicates that the model can be used for the detection of three nutritional components of rainbow trout, and can achieve non-destructive and rapid detection of the nutritional components of rainbow trout, saving detection costs and shortening the detection cycle. |
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