A portable NIR spectroscopy device (Isuzu Optics Corp., Zhubei, Taiwan) was used to collect spectral information of citrus peels in reflectance mode. The machine mainly consists of four sections including a spectrograph (covering spectral range of 900–1700 nm, 1 mm InGaAs detector), a ring-shaped Halogen lamp (20 W), a glass plate (diameter, 60 mm; height, 10 mm). and spectral analysis software (NIRez 2.0 Rice, Isuzu Optics Corp., Taiwan). The device was operated by setting the exposure time of 0.63 ms and the scan number of 5. Before the spectral collection of samples, the device was calibrated by scanning a white tile bar with reflectance of 99.99% and then turning off the light source to ensure 0.00% reflectance.
Before each test, several citrus fruit were taken out and one piece of peel with size of 20 mm × 20 mm (length × width) from each citrus fruit was cut to put into the glass plate of NIR device. Each citrus peel was scanned five times to obtain the average spectra. Finally, a total of 249 spectra of citrus peel samples were prepared for further analysis. The spectra in the range of 912–1667 nm (375 wavelengths) was only considered and analyzed, because of obvious noises existed in the two regions of 900–912 nm and 1667–1700 nm.
The process of spectral collection is always negatively influenced by several factors such as sample status, light scattering, stray light, baseline drift, instrument response and the surrounding environment [33 (link)]. Therefore, it is quite necessary to perform spectra preprocessing to minimize or even eliminate the undesirable effects, improving the signal-to-noise ratio of spectra and predictive ability of subsequent constructed model. In this study, six preprocessing methods including SGS, NC, MSC, 1st Der, 2nd Der, BC, SNV, and MCT were applied to preprocess the collected raw spectra, respectively.
SGS uses polynomials to achieve data smoothing, based on the PLS algorithm, retaining useful information in signal analysis and eliminating random noise [34 (link)]. NC is used to eliminate influence of changes in optical path or sample dilution on spectra [35 (link)]. MSC can eliminate noises caused by specular reflection and non-uniformity of sample, spectral baseline drift and non-repeatability [36 (link)]. Derivation is an effective preprocessing method used to eliminate baseline drift and improve spectral resolution. The 1st Der and 2nd Der can remove the constant baseline and the first functional baseline, respectively [37 (link)]. BC can effectively correct drifts originated from electronic offset, dark current and readout noise [38 (link)]. SNV is applied to reduce influences of uneven particle size and non-specific scattering of particle surface [39 (link)]. MCT is realized using sample spectra minus mean spectra of calibration set to increase the difference between sample spectra, thus improving robustness and prediction ability of model [40 (link)].
All the spectral preprocessing were completed using software Unscramble 10.3X (CAMO, Oslo, Norway).
Before each test, several citrus fruit were taken out and one piece of peel with size of 20 mm × 20 mm (length × width) from each citrus fruit was cut to put into the glass plate of NIR device. Each citrus peel was scanned five times to obtain the average spectra. Finally, a total of 249 spectra of citrus peel samples were prepared for further analysis. The spectra in the range of 912–1667 nm (375 wavelengths) was only considered and analyzed, because of obvious noises existed in the two regions of 900–912 nm and 1667–1700 nm.
The process of spectral collection is always negatively influenced by several factors such as sample status, light scattering, stray light, baseline drift, instrument response and the surrounding environment [33 (link)]. Therefore, it is quite necessary to perform spectra preprocessing to minimize or even eliminate the undesirable effects, improving the signal-to-noise ratio of spectra and predictive ability of subsequent constructed model. In this study, six preprocessing methods including SGS, NC, MSC, 1st Der, 2nd Der, BC, SNV, and MCT were applied to preprocess the collected raw spectra, respectively.
SGS uses polynomials to achieve data smoothing, based on the PLS algorithm, retaining useful information in signal analysis and eliminating random noise [34 (link)]. NC is used to eliminate influence of changes in optical path or sample dilution on spectra [35 (link)]. MSC can eliminate noises caused by specular reflection and non-uniformity of sample, spectral baseline drift and non-repeatability [36 (link)]. Derivation is an effective preprocessing method used to eliminate baseline drift and improve spectral resolution. The 1st Der and 2nd Der can remove the constant baseline and the first functional baseline, respectively [37 (link)]. BC can effectively correct drifts originated from electronic offset, dark current and readout noise [38 (link)]. SNV is applied to reduce influences of uneven particle size and non-specific scattering of particle surface [39 (link)]. MCT is realized using sample spectra minus mean spectra of calibration set to increase the difference between sample spectra, thus improving robustness and prediction ability of model [40 (link)].
All the spectral preprocessing were completed using software Unscramble 10.3X (CAMO, Oslo, Norway).
Free full text: Click here
