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Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits*

机译:傅里叶变换近红外光谱仪在完整柑橘类水果可溶性固形物含量快速估算中的应用*

摘要

Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression (PCR) and partial least squares (PLS) regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79 °Brix.
机译:利用通过光纤收集的傅里叶变换近红外反射(FT-NIR)测量方法,开发了一种无损测量柑橘类水果可溶性固形物含量(SSC)的方法。建立并评估了描述SSC与柑桔NIR光谱之间关系的模型。在这项研究中使用了不同的频谱校正算法(标准正态变量(SNV),乘性信号校正(MSC))。通过主成分回归(PCR)和偏最小二乘(PLS)回归方法分析了柑橘类水果实验室SSC和FT-NIR光谱之间的关系。在本研究中比较了基于不同光谱范围的模型。将一阶导数和二阶导数应用于所有光谱,以减少样本大小,光散射,仪器噪声等的影响。采用了不同的基线校正方法来提高光谱数据的质量。其中,基线校正后的二阶导数方法产生了最佳的噪声消除能力,并产生了最佳的校准模型。总共获取了170个NIR光谱。 135个近红外光谱用于建立校准模型。其余光谱用于验证模型。建立的描述SSC和NIR反射光谱之间关系的PLS模型可以预测35个样品的SSC,相关系数为0.995,RMSEP为0.79°Brix。

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