研究目的
To test the use of visible/near infrared (vis/NIR) reflectance spectroscopy (400–1000 nm) to objectively evaluate the quality parameters of A. bisporus mushrooms.
研究成果
The results of the study demonstrate the applicability of vis/NIR spectroscopy on A. bisporus as a rapid technique to monitor the productive process directly at the company, to standardize the harvest moment, and to support DC’s buyers’ choices. The PLS models for the prediction of the firmness from the cap and the prediction of the WC from the stipe were encouraging.
研究不足
The study was limited by the variability of samples due to the accelerated shelf-life program, which increased the variability of the sample and reduced the prediction capacity of the WC and SSC using a vis/NIR device. The color variation during the simulated shelf-life period also affected the results.
1:Experimental Design and Method Selection:
The study used visible/near infrared (vis/NIR) reflectance spectroscopy (400–1000 nm) to evaluate the quality parameters of A. bisporus mushrooms. The vis/NIR spectra were correlated to reference measures to build predictive models using the partial least squares regression method.
2:Sample Selection and Data Sources:
A total of 167 samples of A. bisporus mushrooms were harvested according to the main DC purchasing standards. The samples were analyzed the day of sampling just before the physico-chemical analyses.
3:List of Experimental Equipment and Materials:
A portable vis/NIR spectrometer (JAZ vis/NIR spectrometer, OceanOptics, Inc., Dunedin, FL, USA) operating in the wavelength range 400–1000 nm was used for spectral acquisitions.
4:Experimental Procedures and Operational Workflow:
Spectral acquisitions were made in reflectance mode using a bifurcated (Y-shaped) fibre optic probe for reflection measurement. The spectra were acquired at four positions (two from the cap and two from the stipe) from each sample.
5:Data Analysis Methods:
The vis/NIR spectra were analyzed using The Unscrambler 9.8 software package (Camo Software, Oslo, Norway) to calculate the predictive models. PCA and PLS regression were used for data analysis.
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