Short-wave Near-infrared Spectroscopy Research Articles

Rapid Discrimination of Fish Feeds Brands Based on Visible and Short-Wave Near-Infrared Spectroscopy

Visible and short-wave near-infrared spectroscopy (Vis/SW-NIRS) was investigated in this study to distinguish four different brands of granular and ground fish feeds. A total of 240 samples were prepared for spectra collecting from a field spectroradiometer (325–1,075 nm), 160 of which were randomly selected to create the calibration model, and the rest 80 ones to verify the model. Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC), and Savitzky–Golay smoothing were adopted to eliminate the system noises and external disturbances. Then partial least squares analysis was implemented for calibration models. For granular samples, the discrimination ability of SNV and MSC was better than smoothing. However, for ground samples, the recognition ability of smoothing surpassed SNV and MSC. Moreover, for threshold values of ±0.1 and ±0.2, the average discrimination accuracy rates were 85.8% and 97.5%, respectively, for four granular samples and 95.4% and 100%, respectively, for ground samples, which signified the better discrimination performances of ground samples than granular ones. The results indicated that Vis/SW-NIRS technique could be promising to be applied as a rapid and highly accurate way for the qualitative discrimination of fish feeds brands.

Nondestructive Differentiation of Panax Species Using Visible and Shortwave Near-Infrared Spectroscopy

Visible and short-wave near-infrared (Vis-SWNIR) spectroscopy was investigated to differentiate species of panax, including American Panax quinquefoliumI, Chinese Panax quinquefolium, and Chinese Panax ginseng. Principal component analysis (PCA) was applied before least-square support vector machine (LS-SVM) modeling, and the vast points of the spectral data (376–1,025 nm) were effectively reduced. PCA-LS-SVM differentiated species with 100% correct classification rate for the tested samples. In addition, effective wavelengths were selected according to modeling power, discrimination power, regression coefficients, loading weights, and genetic algorithms, respectively. The optimal and simplified LS-SVM model with 100% correct classification rate was achieved using the effective wavelengths selected by genetic algorithms. The results showed that Vis-SWNIR spectroscopy technique can be applied as a high accuracy and fast way for the qualitative discrimination of herb species.

Rapid determination of ethylene content in tomatoes using visible and short-wave near-infrared spectroscopy and wavelength selection

The plant hormone ethylene controls many aspects of development. In tomato, ethylene is an essential component for fruit ripening. In this paper, the study was concentrated on the visible/short-wave near-infrared (Vis/SW NIR) spectroscopy technique for the quantitative analysis of ethylene content in three varieties of tomatoes: non-transgenic tomatoes, and transgenic tomatoes with antisense LeETR1 and LeETR2. The results indicate that the determination of ethylene content in tomatoes could be successfully performed through Vis/SW NIR spectroscopy combined with chemometrics methods including partial least square regression (PLSR) and stepwise multiple linear regression (SMLR). The performances of models using four spectral pretreatment methods – standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivation – were compared. Wavelengths for ethylene analysis in tomatoes were proposed using SMLR models. The models of ethylene in tomato using the assigned wavelengths show reliable results. The prediction precision of PLSR and SMLR using selected wavelengths was compared. The results show that the modeling of PLSR using the visible region needed less time than that using full region, although the prediction precisions were a little lower. The overall results indicate that Vis/SW NIR spectroscopy combined with wavelength selection could be applied as a nondestructive and rapid tool for the determination of ethylene content in tomatoes.

Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics

The feasibility of using visible and short-wave near-infrared (Vis/SW NIR) diffuse reflectance spectroscopic technique in the 400–1000 nm region to classify tomatoes with different genotypes was investigated. The discrimination performance of different chemometrics techniques, including least-squares support vector machines (LS-SVM), discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), discriminant partial least-squares (DPLS) was compared. The results show that LS-SVM has the same performance, 100% of success rate, which is the same with DA but requires less time if a large variety of tomatoes are to be discriminated according to genotypes. Selecting the most important wavelengths that affect the classification rate most was proposed. The results show that the overall identification rate by LS-SVM method decreases from 100% to 96.81% in validation set using the selected wavelengths, which is acceptable. The importance of these conclusions may be helpful to transfer Vis/SW NIR technology from the laboratory to the industrial world for on-line and portable application.

Assessment of Potato Dry Matter Concentration Using Short-Wave Near-Infrared Spectroscopy

The utility of short-wavelength near-infrared spectroscopy (over the wavelength region 750–950 nm), used in a partial transmittance optical geometry, was assessed as a means of estimating the dry matter concentration of potato tubers. The sampling optics did not involve contact with the sample, and could be used on a moving stream of product. A prediction accuracy of R 2 (correlation coefficient of determination) of 0.85 with a root mean square error of prediction (RMSEP) of 1.52% for intact, whole tubers and R 2 = 0.95 and RMSEP = 0.50% for sliced tubers was achieved. We conclude that short-wavelength near-infrared technology using a partial transmittance optical sampling geometry can be a useful tool for rapid assessment of tuber dry matter concentration prior to processing.

Short-Wave Near-Infrared Spectroscopy for Rapid Quantification of Acidity of Aviation Kerosene

The application of short-wave near-infrared spectroscopy (SW-NIRS) in acidity of aviation kerosene was de- scribed on the term of chemometrics. This spectral region is interesting because this type of spectrometer reduces costs in comparison with traditional long-wave near-infrared (LW-NIR) instrument. The SW-NIR calibration models of aviation kerosene were established by partial least square regression (PLS). Satisfactory calibration statistics were obtained with a standard error of calibration (SEC) of 0.028 mgKOH/100mL and a standard error of prediction (SEP) of 0.036 mgKOH/100mL. A t-test showed that the NIRS model had an accuracy equivalent to that of GB/T 264-91, which was equal to ASTM D 847, in the analysis of acidity of aviation kerosene. The NIRS method had the advantages of rapid analysis, better repeatability, little simple, and online analysis, etc.

Short-Wave Near-Infrared Spectroscopy of Milk Powder for Brand Identification and Component Analysis

The aim of the present paper was to provide new insight into the short-wave near-infrared (NIR) spectroscopic analysis of milk powder. Near-infrared spectra in the 800- to 1,025-nm region of 350 samples were analyzed to determine the brands and quality of milk powders. Brand identification was done by a least squares support vector machine (LS-SVM) model coupled with fast fixed-point independent component analysis (ICA). The correct answer rate of the ICA-LS-SVM model reached as high as 98%, which was better than that of the LS-SVM (95%). Contents of fat, protein, and carbohydrate were determined by the LS-SVM and ICA-LS-SVM models. Both processes offered good determination performance for analyzing the main components in milk powder based on short-wave NIR spectra. The coefficients of determination for prediction and root mean square error of prediction of ICA-LS-SVM were 0.983, 0.231, and 0.982, and 0.161, 0.980, and 0.410, respectively, for the 3 components. However, there were less than 10 input variables in the ICA-LS-SVM model compared with 225 in the LS-SVM model. Thus, the processing time was much shorter and the model was simpler. The results presented in this paper demonstrate that the short-wave NIR region is promising for fast and reliable determination of the brand and main components in milk powder.

Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment

In this study, short-wave near-infrared (NIR) spectroscopy at 800–1050 nm region was investigated for the analysis of main compounds in milk powder. Through quantitative analysis, the feasibility is further demonstrated for the simultaneous measurement of fat, proteins and carbohydrate in milk powder. Two models, partial least-squares and least-squares support vector machine, were compared and utilized for regression coefficients and loading weights. The affect of standard normal variate spectral pretreatment to model performance was evaluated. Based on the resulted coefficients and loading weights, interesting wavelength regions of nutrition in milk powder are screened and the assignment of all specific wavelengths is firstly proposed in the details associated with chemical base. Instead of the whole short-wave NIR spectral data, these assigned wavelengths which can be reliably exploited were used for the content determination. Compared with other spectroscopy technique, assigned short-wave NIR spectral wavelengths did a good work. Determination coefficients for prediction are 0.981, 0.984, and 0.982, respectively for three components. The proposed wavelength assignment in the short-wave NIR region could be used for the component contents determination of milk powder, and could be as a guidance to interpret the spectra of milk powder.

Nondestructive quantitative analysis of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy combined with radial basis function neural networks

A new assay method for the nondestructive determination of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy (NIR) combined with radial basis function (RBF) neural networks is investigated. The modern near-infrared spectroscopy analysis technique is efficient, simple and nondestructive, which has been used in chemical analysis in diverse fields. Short-wave NIR is a more rapid, flexible, and cost-effective method to control product concentration in pharmaceutical industry. The RBF neural networks are local approximation networks that have superiorities in function approximation and learning speed. In addition, the structure of RBF networks is simple. Estimate and calibration of the sample concentration via short-wave NIR are made with the aid of RBF models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra. Various optimum models of them are established and compared. Experiment results show that the models of SNV spectra can give better performance, and the optimized RBF neural network model after SNV treatment were given, by which the root-mean-square-errors (RMSE) for calibration set and test set were 0.3266% and 0.5244%, respectively and the correlation coefficients ( R) for calibration set and test set were 0.9942 and 0.9852, respectively. The proposed RBF method based on short-wave NIR is more valuable and economical for quantitative analysis than traditional methods such as partial least squares (PLS).

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Determination of alcohol content in beverages using short-wave near-infrared spectroscopy and temperature correction by transfer calibration procedures.

This paper reports the utilization of short-wave near-infrared (SW-NIR) transmission spectroscopy for rapid and conclusive analysis of alcoholic content (% v/v) in beverages. This spectral region is interesting because common visible diode array spectrometers can be utilized, reducing time and costs in comparison with traditional near-infrared or mid-infrared instruments. A correction of external temperature influence is necessary, and for this purposes two calibration transfer procedures were compared: piecewise direct standardization (PDS) and orthogonal signal correction (OSC). The RMSEP found for the alcoholic content model at 20 degrees C was 0.13% v/v and, after application of transfer calibration procedures at other temperatures (15, 25, 30 and 35 degrees C) using the model built at 20 degrees C, errors of the same order of magnitude were obtained.

Selection of optimal process analyzers for plant-wide monitoring.

In this paper, the effect of process analyzer selection and positioning on plant-wide process monitoring is investigated. A fundamental problem in process analytical chemistry is the incomparability of different instrument characteristics. A fast but imprecise instrument is incomparable to a slow but precise instrument. Theory is developed to overcome this problem by using an abstract definition of a process analyzer. This definition allows us to put all instrument characteristics for a particular monitoring task on an equal footing. This results in a measurability factor M that expresses monitoring performance of any process measurement by combining instrument characteristics such as precision, sampling rate, grab size, response correlation, and delay time. Both the choice of location and the performance characteristics of different process analyzers can be evaluated using the measurability factor. The unifying nature of the measurability factor allows for a rational decision between completely different process analyzers and locations (Smilde et al., in this issue). The theory is illustrated and validated with an experiment. A tubular reactor for free-radical bulk polymerization of styrene is monitored by in-line short-wave near-infrared spectroscopy at different positions. Alternatively, product samples are collected for at-line near-infrared analysis. Both analyzers measure styrene monomer concentration. The analysis results are used to predict conversion as well as number and weight average molecular mass of the polystyrene reactor product. The theoretical measurability factors for this case study correspond well with the experimental findings.

Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment.

The present study has aimed at providing new insight into short-wave near-infrared (NIR) spectroscopy of biological fluids. To do that, we analyzed NIR spectra in the 800-1,100-nm region of 100 raw milk samples. The contents of fat, proteins, and lactose were predicted by partial least-squares (PLS) regression and band assignment in that region was investigated based upon PLS loading plots and regression coefficients. For the fat prediction, the whole set of samples was divided into two groups and the fat concentration was predicted for the samples that were not included in the calibration procedures. The correlation coefficient and root-mean-square error of prediction (RM-SEP) in the better prediction run were found to be 0.996 and 0.087 wt %, respectively. Assignment of the bands due to fat was proposed based upon the regression coefficients and PLS loading weights, and the importance of a pretreatment in the prediction was discussed. Milk proteins also yielded sufficient correlation coefficients and RMSEP although the contributions of protein bands to the milk spectra were much smaller than those of the fat bands. The sizes of the calibration models for protein prediction were considered. This is the first time that good correlation coefficients and RMSEP of proteins have ever been obtained for the short-wave NIR spectra of milk. For lactose, noisy regression coefficients with limited prediction ability were obtained. Band assignment was investigated also for bands due to proteins and lactose. We propose the detailed band assignment for the short-wave NIR region useful for various biological fluids. The results presented here demonstrate that the short-wave NIR region is promising for the fast and reliable determination of major components in biological and biomedical fluids.