Visible Short-wave Near-infrared Spectroscopy Research Articles

Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy

Abstract Visible–shortwave near-infrared spectroscopy has been used for internal quality measurement, but the optical penetration to the thickness of fruit skin becomes a challenge. This research aimed to develop partial least square regression model for the soluble solid content (SSC) measurement of fruits having various skin thicknesses, namely dragon fruit, tomato, guava, sapodilla, and banana. The spectra of each fruit were taken in a reflectance mode over a wavelength range of 400–1,000 nm. The best models obtained from banana and sapodilla yielded determination coefficient of prediction (R 2 p) of 0.88 and 0.90 and root mean square error of prediction (RMSEP) 0.39 and 0.38°Brix, respectively. The banana and sapodilla SSC prediction models should be able to be used carefully in a variety of applications. Tomato and guava had moderately thinner skin but had the lower R 2 p of 0.64 and 0.76 and the RMSEP of 0.17 and 0.26°Brix, respectively. The poorest model was yielded by dragon fruit, which had the thickest skin with the R 2 p of 0.59 and the RMSEP of 0.40°Brix. The model for guava, although having low R 2 p, can still be utilized as a screening criterion and in some other ‘approximate’ applications. However, the SSC prediction model for tomatoes and dragon fruit is not recommended to use and requires additional research. In addition to the effect of skin thickness, other fruit morphological influences the result of this study. Internal structure and seed number influence the reflection optical geometry, which also affects the SSC prediction model.

Detection of deltamethrin in cabbages using visible shortwave near-infrared spectroscopy

Pesticides have long been used in the cabbage industry to control pest infestation. This study investigated the potential application of low-cost and portable visible shortwave near-infrared spectroscopy for the detection of deltamethrin residue in cabbages. A total of sixty organic cabbage samples were used. The sample was divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining batch was not sprayed (control sample). The first three batches of the cabbages were sprayed with the pesticide at three different concentrations, namely low, medium and high with the values of 0.08, 0.11 and 0.14% volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near-infrared (VSNIR) spectrometer with wavelengths range between 200 and 1100 nm. Gas chromatography-electron capture detector (GC-ECD) was used to determine the concentration of deltamethrin residues in the cabbages. Partial least square (PLS) regression method was adopted to investigate the relationship between the spectral data and deltamethrin concentration values. The calibration model produced the values of coefficient of determination (R2 ) and the root mean square error of calibration (RMSEC) of 0.98 and 0.02, respectively. For the prediction model, the values of R2 and the root mean square error of prediction (RMSEP) were 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement is a promising technique for the detection of pesticide at different concentrations in cabbage samples.

Point-of-care Vis-SWNIR spectroscopy towards reagent-less hemogram analysis

Current chemometrics and artificial intelligence methods are unable to deal with complex multi-scale interference of blood constituents in visible shortwave near-infrared spectroscopy point-of-care technologies. The major difficulty is to access the rich information in the spectroscopy signal, unscrambling and interpreting spectral interference to provide analytical quality quantifications. We present a new self-learning artificial intelligence method for spectral processing based on the search of covariance modes with direct correspondence to the Beer-Lambert law. Dog and cat hemograms were analyzed by impedance flow cytometry and standard laboratory methods (erythrocytes counts, hemoglobin, and hematocrit). Spectral records were performed for the same samples. The methodology was benchmarked against state-of-the-art chemometrics: a multivariate linear model of hemoglobin bands, similarity, partial least squares, local partial least squares, and artificial neural networks.The new method outperforms the state-of-the-art, providing analytical quality quantifications according to desired veterinary pathology guidelines (total errors of 1.69% to 7.14%), whereas chemometric methods cannot. The method finds relevant samples and spectral information that hold the quantitative information for a particular interference mode, in contrast to the current methods that do not hold a relationship with the Beer-Lambert law. It allows the interpretation of interference bands used in quantification, providing the capacity to determine if the composition of an unknown sample is predictable. This research is especially relevant for improving current optical point-of-care technologies that are affected by spectral interference and moving towards micro-sampling and reagent-less technologies in healthcare and veterinary medicine diagnosis.

Spectral Multivariable Selection and Calibration in Visible-Shortwave Near-Infrared Spectroscopy for Non-Destructive Protein Assessment of Spirulina Microalga Powder

Visible and shortwave-near-infrared spectroscopy was used to predict protein content in Spirulina powder. Three variable selection algorithms of partial least square based uninformative variable elimination, partial least square based genetic algorithm, and successive projections algorithm were analyzed. Results showed that it was necessary to operate partial least square based uninformative variable elimination before successive projections algorithm. Successive projections algorithm performed better than partial least square based genetic algorithm, because it had a better result and fewer selected variables. The results showed that partial least square based uninformative variable elimination-successive projections algorithm was a good hybrid algorithm for the multivariable selection, and it was feasible for using visible and shortwave-near-infrared spectroscopy to non-destructively estimate protein content in Spirulina powder.

Fast Nondestructive Measurement and Discrimination of Tablet Based on Vis/NIR Spectroscopy and Chemometrics

This paper presents methods based on chemometrics analysis to select the optimal model for variety discrimination of ginkgo (Ginkgo biloba L.) tablets by using a visible/short-wave near-infrared spectroscopy (Vis/NIRS) system. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325-1075nm using a spectrograph. Principal component artificial neural network (PC-ANN) was used to identify the tablet varieties. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN. Independent component analysis (ICA) was executed to select several optimal wavelengths based on loading weights. The absorbance values log (1/R), corresponding to the wavelengths of 481nm, 1000nm, 460nm, 572nm, 658nm, 401nm, 998nm, 996nm, 468nm and 661nm were then chosen as the input data of artificial neural network (IC-ANN), and the discrimination rate was reached at 95.6%, which was better than PC-ANN. The results indicated that ginkgo tablets discrimination was good based on the both methods.