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Application of NIR Reflectance Spectroscopy on Determination of Moisture Content of Peanuts: A Non Destructive Analysis Method

机译:NIR反射光谱法在花生水分含量测定中的应用:非破坏性分析方法

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NIR spectroscopy was used to measure the moisture content of Virginia and Valencia type in-shell peanuts. Peanuts were conditioned to various moisture levels between 7 and 26 % (wet basis) and the moisture content verified using a standard oven methodon a sub-sample of each level. The various moisture levels were separated as calibration and validation sets of samples. NIR absorption spectra data from 400 to 2500 nm were collected from peanuts within the calibration and validation sample sets. Measurements were obtained on 30 replicates within each moisture level. Spectral data were mathematically treated to get its derivative function. Partial Least Square (PLS) analysis was performed on the calibration set and models were developed using the rawspectral data and its derivative function data. These models were used to predict the in-shell moisture content of peanuts in the validation sample set. The Standard Error of Calibration (SEC) and R2 of the calibration models were calculated to select the best calibration model. Predicted and reference moisture contents were compared. Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were calculated to validate the calibration models
机译:NIR光谱法用于测量弗吉尼亚州和瓦伦西亚型壳体花生型的水分含量。花生被调节到7-26%(湿基础)之间的各种水分水平,并且使用标准炉法验证的水分含量是每个级别的子样本。将各种水分水平分开为校准和验证样品。从校准和验证样本集中的花生收集来自400至2500nm的NIR吸收光谱数据。在每个水分水平的30个重复上获得测量。在数学上处理频谱数据以获得其衍生功能。在校准集上执行局部最小正方形(PLS)分析,并使用原始光谱数据及其衍生功能数据开发模型。这些模型用于预测验证样品集中的花生的壳体水分含量。计算校准的标准误差(SEC)和校准模型的R2以选择最佳校准模型。比较预测和参考水分含量。计算相对百分比偏差(RPD)和预测标准误差(SEP)以验证校准模型

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