Short-wave Near-infrared Spectroscopy Research Articles

Short-Wave Near-Infrared Spectroscopy and Chemometrics to Detect Adulteration of Honey with Sucrose Syrup

Near-infrared spectroscopy (NIRS) coupled with multivariate statistical analysis was assessed as a rapid method of detecting honey adulteration in the Philippines. The sample set contained 288 spectra of authentic and adulterated honey samples obtained from six farms in the provinces of Benguet and La Union. Spectral data were pre-processed using ParLeS software in which partial least squares regression (PLSR) analysis was employed. Multivariate analyses like PLSR, principal component analysis (PCA) and linear discriminant analysis (LDA) were performed to predict the level of adulteration, reducing sugar content, and apparent sucrose content. Calibration models for predicting adulteration level (from none to highly adulterated) gave the best results using PLSR on spectral data without pretreatment (calibration R2 > 0.93, validation R2 > 0.94). Principal components (PCs) of the spectral data were extracted using PCA wherein the NIR absorbance bands for sugar (836-840 nm and 978-980 nm) and water (938-940 nm, 978-980 nm, 984-986 nm and 992-994 nm) in honey were identified. Using LDA, calibration models were able to classify honey based on level of adulteration and apparent sucrose; overall accuracies of 99.5% and 100% were observed, respectively. When validated, however, the LDA models could not detect pure honey (out of 6 and 11 samples, respectively), while 86.7% and 100% of adulterated honey could be detected, respectively. Further sampling is recommended to strengthen LDA models.

Portable LWNIR and SWNIR spectroscopy with pattern recognition technology for accurate and nondestructive detection of hidden mold infection in citrus

An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy (NIRDRS) and chemometric methods. Penetrability of NIRDRS light on the peel of Chunjian hybrid citrus was studied. The results show that NIRDRS light can penetrate the peel to a certain extent, while the penetrability of short-wave near infrared (SWNIR) was better than that of long-wave near infrared (LWNIR). The identification models of hidden mold infection were established with five pattern recognition methods combined with different wavelength bands. The results show that the identification models of LWNIR were much better than those of SWNIR. 100% identification accuracies of LWNIR were obtained with soft independent pattern classification (SIMCA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and the optimized pretreatment methods. In addition, the developed models were further validated by the external validation set collected one month later.

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.

Reliable classification of nutmeg fruits from some geographical origins using SWNIR spectroscopy associated with PLS-DA and SVM methods: Case Study in Aceh Province, Indonesia

Essential oils of nutmeg (Myristica fragrans) can have different qualities due to the soil and industrial process, as Indonesia’s main nutmeg production areas are enormous, especially in Aceh province. The lack of information on detecting chemical constituents and classifying nutmeg from Aceh province makes the price of nutmeg from Aceh unable to compete. Therefore, this study aims to reliably classify nutmeg fruits of some geographical origins in Aceh Province using shortwave near-infrared (SWNIR) spectroscopy associated with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). The SWNIR used in this study has a wavelength of 381 to 1065 nm at a resolution of 3 nm and a diffuse reflectance mode. The fruit comes from four geographical areas of origin in the province of Aceh. A total of forty SWNIR spectral data were further classified using two algorithms, namely PLS-DA and SVM. The results show that SWNIR can correctly classify nutmeg from some sub-districts in Aceh province with the help of the PLS-DA and SVM algorithms. From this study, the following SVM algorithm is recommended to be used to classify nutmeg from the province of Aceh with an outstanding level of precision.

Ripening assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) on tree by SW-NIR reflectance spectroscopy-based calibration models

The aim of this study was the non-destructive assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) ripening, based on the prediction of internal quality attributes (IQA) by short-wave near-infrared reflectance spectroscopy (SW-NIRS) calibration models. Spectra from fruit of 50 trees located in two different orchards, were acquired on tree using a customized portable visible near-infrared (vis-NIR) system. Partial least squares (PLS) was used to build the various IQA calibration models. The models were tested through internal validation (IV) and external validation (EV). Generally, the IV results were always superior to those of EV: regarding IV, a high regression coefficient (R2) and low root mean square error of prediction (RMSEP) were achieved, revealing a good predictive performance for juice pH (R2 = 0.80; RMSEP = 0.10; SDR = 2.23), soluble solids content (SSC) (R2 = 0.79; RMSEP = 0.75 %; SDR = 2.27), titratable acidity (TA) (R² = 0.73; RMSEP = 0.24 % citric acid; SDR = 1.94) and the maturation index (MI) (R2 = 0.80; RMSEP = 1.38; SDR = 2.2). The best EV predictions were obtained for TA (R2 = 0.69; RMSEP = 0.38 % citric acid; SDR = 1.24), and MI (R2 = 0.69; RMSEP = 2.07; SDR = 1.49). Calibration models for glucose, fructose and sucrose showed medium-coarse predictions for both validation strategies. A detailed investigation of MI models was performed, to understand the causes of their poor EV results. In the context of EV, model updating strategies were explored by using some validation samples to improve the calibration model. The methods of bias correction and spiking were tested, showing a clear improvement in the predictions.

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.

Quantification of potassium concentration with Vis–SWNIR spectroscopy in fresh lettuce

Chronic kidney disease (CKD) is becoming a major public health problem worldwide, and excessive potassium intake is a health threat to patients with CKD. In this study, visible–short-wave near-infrared (Vis–SWNIR) spectroscopy and chemometric algorithms were investigated as nondestructive methods for assessing the potassium concentration in fresh lettuce to benefit the CKD patients’ health. Interactance and transmittance measurements were performed and the competencies were compared based on the multivariate methods of partial least-square regression (PLS) and support vector machine regression (SVR). Meanwhile, several preprocessing methods [first- and second-order derivatives in combination with standard normal variate (SNV)] and wavelength selection method of competitive adaptive reweighted sampling (CARS) were applied to eliminate noise and highlight the spectral characteristics. The PLS models yielded better prediction than the SVR models with higher correlation coefficients ([Formula: see text]) and residual predictive deviation (RPD), and lower root-mean-square error of prediction (RMSEP). Excellent prediction of green leaves was obtained by the interactance measurement with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and [Formula: see text]; while the transmittance spectra of petioles provided optimal prediction with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and RPD[Formula: see text]=[Formula: see text]3.34, respectively. Therefore, the results indicated that Vis–SWNIR spectroscopy is capable of intelligently detecting potassium concentration in fresh lettuce to benefit CKD patients around the world in maintaining and enhancing their health.

Shortwave Near-Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 Contamination in Polished Rice

The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B1 (AFB1) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB1 and 15 samples artificially contaminated with AFB1. Quantitative calibration models to detect AFB1 were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 μg/kg, and a bias of -0.778 μg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 μg/kg, bias = 0.015 μg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 μg/kg, bias = 0.008 μg/kg). A classification-based partial least squares discriminant analysis model of AFB1 contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB1 contamination.

Validation of short wave near infrared calibration models for the quality and ripening of ‘Newhall’ orange on tree across years and orchards

The aim of this research was to test the viability of short wave near infrared spectroscopy (SW-NIRS) for the monitoring of fruit quality and ripening evolution in Algarve Citrus orchards (Citrus sinensis L. Osbeck ‘Newhall’). Specifically, we have investigated the robustness of SW-NIRS calibration models in real conditions, that is: i) measurements were performed on tree, at a single location in the fruit equator, in the sunlight and with no temperature equilibration; and ii) with validation through independent data obtained in different years and/or orchards. Calibration models for soluble solids content (SSC), juice pH, titratable acidity (TA), firmness and maturation index (MI = SSC/TA) were built from the spectral data obtained in two orchards with different edaphoclimatic conditions, and in two consecutive years, corresponding to four independent datasets. We propose a method to assess model robustness through the comparison of internal validation (IV: calibration and validation data sets homogeneously sampled from the whole data set) and external validation (EV: calibration and validation data sets corresponding to different orchards and/or years). The method is based on the statistics of the results obtained by either IV and EV when applied to all the possible combinations of the four datasets. The results show that IV overestimates the models’ performance relatively to the realistic exercise of EV. Globally, SSC and juice pH were the best performing models, with fair performances in IV and poor performances in EV (example for SSC: (IV/EV): rmsep = 1.00/1.15%, SDR = 1.40/1.13, R2 = 0.49/0.34). Firmness yielded the worse models, while TA and MI yielded intermediate performances. However, the plots derived from the comparison method suggest a convergence of IV and EV performances for larger numbers of samples, and thus the potential for future continuous model improvement.

Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring.

Conventional wastewater treatment generates large amounts of organic matter–rich sludge that requires adequate treatment to avoid public health and environmental problems. The mixture of wastewater sludge and some bulking agents produces a biosolid to be composted at adequate composting facilities. The composting process is chemically and microbiologically complex and requires an adequate aeration of the biosolid (e.g., with a turner machine) for proper maturation of the compost. Adequate (near) real-time monitoring of the compost maturity process is highly difficult and the operation of composting facilities is not as automatized as other industrial processes. Spectroscopic analysis of compost samples has been successfully employed for compost maturity assessment but the preparation of the solid compost samples is difficult and time-consuming. This manuscript presents a methodology based on a combination of a less time-consuming compost sample preparation and ultraviolet, visible and short-wave near-infrared spectroscopy. Spectroscopic measurements were performed with liquid compost extract instead of solid compost samples. Partial least square (PLS) models were developed to quantify chemical fractions commonly employed for compost maturity assessment. Effective regression models were obtained for total organic matter (residual predictive deviation—RPD = 2.68), humification ratio (RPD = 2.23), total exchangeable carbon (RPD = 2.07) and total organic carbon (RPD = 1.66) with a modular and cost-effective visible and near infrared (VNIR) spectroradiometer. This combination of a less time-consuming compost sample preparation with a versatile sensor system provides an easy-to-implement, efficient and cost-effective protocol for compost maturity assessment and near-real-time monitoring.

Determining the composition of ammonia/water mixtures using short-wave near-infrared spectroscopy

This paper proposes a methodology based on short-wave near-infrared spectroscopy to determine the ammonia content of ammonia/water mixtures with ammonia mass fraction in the range 0.35–0.65. Establishing this methodology meant modeling the relationship between the pressure bar (15–25)bar, temperature (20–50)°C and composition of the ammonia–water in the mixture (0.35–0.65 in ammonia mass fraction) with absorbance at 1033nm. The experiments were designed to optimize experimental work. A 23 factorial design+3 center points was used to establish and analyze the significance of the variables in the absorbance using analysis of variance (ANOVA). A linear model for absorbance was obtained using the least squares method. The trueness of the results versus the values obtained was assessed using a reference method; density measurement was chosen for this study. The accuracy of the results in terms of root-mean-square deviation (RMSD) was 3.7%. The methodology proposed represents a fast alternative for the “in-situ” measurement of the ammonia composition of ammonia–water mixtures in absorption refrigeration systems.

Qualitative and simultaneous quantitative analysis of cimetidine polymorphs by ultraviolet–visible and shortwave near-infrared diffuse reflectance spectroscopy and multivariate calibration models

The object of the present study was to investigate the feasibility of applying ultraviolet-visible and shortwave near-infrared diffuse reflectance spectroscopy (UV–vis–SWNIR DRS) coupled with chemometrics in qualitative and simultaneous quantitative analysis of drug polymorphs, using cimetidine as a model drug. Three polymorphic forms (A, B and D) and a mixed crystal (M1) of cimetidine, obtained by preparation under different crystallization conditions, were characterized by microscopy, X-ray powder diffraction (XRPD) and infrared spectroscopy (IR). The discriminant models of four forms (A, B, D and M1) were established by discriminant partial least squares (PLS-DA) using different pretreated spectra. The R and RMSEP of samples in the prediction set by discriminant model with original spectra were 0.9959 and 0.1004. Among the quantitative models of binary mixtures (A and D) established by partial least squares (PLS) and least squares-support vector machine (LS-SVM) with different pretreated spectra, the LS-SVM models based on original and MSC spectra had better prediction effect with a R of 1.0000 and a RMSEP of 0.0134 for form A, and a R of 1.0000 and a RMSEP of 0.0024 for form D. For ternary mixtures, the established PLS quantitative models based on normalized spectra had relatively better prediction effect for forms A, B and D with R of 0.9901, 0.9820 and 0.9794 and RMSEP of 0.0471, 0.0529 and 0.0594, respectively. This research indicated that UV–vis–SWNIR DRS can be used as a simple, rapid, nondestructive qualitative and quantitative method for the analysis of drug polymorphs.

Sensory Quality Evaluation of Rice Using Visible and Shortwave Near-Infrared Spectroscopy

This research aimed to develop both visible and shortwave near-infrared spectroscopy to evaluate the sensory qualities of cooked rice. In this study, four different types of milled rice were used: parboiled, white, new Jasmine, and aged Jasmine. The sensory qualities of cooked rice (adhesiveness, hardness, stickiness, dryness, whiteness, and aroma) were evaluated by a trained sensory panel. The results demonstrated that these sensory attributes correlated with visible and shortwave near-infrared spectral data. Both visible and shortwave near-infrared spectroscopy models used for predicting the sensory qualities of cooked rice were established using partial least squares regression. All prediction results for sensory qualities showed a range of R2val between 0.837 and 0.918, with the highest found for aroma (0.918). The proposed models can be utilized in quality control by the rice industry.

Research on Measurement Method of Sucrose Concentration by Short-Wave Near-Infrared Trans-Reflective Spectroscopy

Research on Measurement Method of Sucrose Concentration by Short-Wave Near-Infrared Trans-Reflective Spectroscopy

Monitoring Urban Wastewaters’ Characteristics by Visible and Short Wave Near-Infrared Spectroscopy

On-line monitoring of wastewater parameters is a major scientific and technical challenge because of the great variability of wastewater characteristics and the extreme physical-chemical conditions that endure the sensors. Wastewater treatment plant managers require fast and reliable information about the input sewage and the operation of the different treatment stages. There is a great need for the development of sensors for the continuous monitoring of wastewater parameters. In this sense, several optical systems have been evaluated. This article presents an experimental laboratory-based approach to quantify commonly employed urban wastewater parameters, namely biochemical oxygen demand in five days (BOD5), chemical oxygen demand (COD), total suspended solids (TSS), and the ratio BOD5:COD, with a visible and short wave near infrared (V/SW-NIR) spectrometer (400–1000 nm). Partial least square regression (PLSR) models were developed in order to quantify the wastewater parameters with the recorded spectra. PLSR models were developed for the full spectral range and also for the visible and near infrared spectral ranges separately. Good PLSR models were obtained with the visible spectral range for BOD5 (RER = 9.64), COD (RER = 10.88), and with the full spectral range for the TSS (RER = 9.67). The results of this study show that V/SW-NIR spectroscopy is a suitable technique for on-line monitoring of wastewater parameters.

Grading of apples based on firmness and soluble solids content using Vis/SWNIR spectroscopy and spectral scattering techniques

Sorting of apple fruit based on internal quality can enhance the industry’s competiveness and profitability and assure consumer satisfaction. In this research, visible and shortwave near-infrared (Vis/SWNIR) spectroscopy (460–1100nm) and spectral scattering (450–1050nm) were used for sorting three varieties of apple (i.e., ‘Delicious’, ‘Golden Delicious’ and ‘Jonagold’) into two quality grades based on firmness, soluble solids content (SSC), or both firmness and SSC. Vis/SWNIR spectra were obtained in interactance mode under stationery condition, whereas spectral scattering images were acquired online at a conveyor speed of 82mm/s. A total of 8491 apples for the three varieties harvested from the same orchard in 2009, 2010 and 2011 were used for analysis. First derivative spectra were obtained from the Vis/SWNIR data, while the scattering images were first preprocessed by computing mean reflectance spectra and then performing continuous wavelet transform decompositions. Sorting algorithms were developed using sequential forward selection and linear discriminant analysis, and the classification models were compared in terms of their overall performance and local confusion matrices. A sensitivity analysis was also performed to assess the effect of using different quality threshold criteria on the sorting performance. Overall, consistent and relatively good sorting results for firmness (ranging between 77.9% and 98.2%) and moderate results for SSC (ranging between 62.0% and 91.7%) were obtained using scattering technique. Vis/SWNIR technique showed slightly better sorting results for firmness (ranging between 87.3% and 97.6%) and SSC (ranging between 77.1% and 92.3%). When the classification was performed based on both firmness and SSC, the sorting accuracies generally decreased to between 75.7% and 90.1% for Vis/SWNIR and between 69.7% and 91.5% for spectral scattering. Vis/SWNIR and spectral scattering techniques have potential for online sorting and grading of apples by firmness and SSC.

Biomass and Total Lipid Content Assessment of Microalgal Cultures Using Near and Short Wave Infrared Spectroscopy

The technique of near and short wave near-infrared spectroscopy was assessed with respect to analysis of dry matter and lipid content of microalgae with potential for biodiesel production. Microalgal culture samples were filtered through GF/C filter papers and spectral measurements of wet and oven dried (60 °C overnight) filter papers over the ranges of 300–1,100 nm and 1,100–2,500 nm were recorded. Partial least square models on culture biomass and lipid content for combined species data were poor in terms of RMSECV, R CV and the ratio of RMSECV to SD. A single species model for C. vulgaris based on 1,100–2,500 nm spectra of dry filtrate supported a model with RMSECV, R CV and SDR values of 0.32 g L−1, 0.955 and 3.38 for biomass and 0.089 g L−1, 0.874 and 2.06 with lipid, respectively. However, the dry filtrate models on biomass and lipid content performed poorly in the prediction of samples drawn from an independent series of C. vulgaris cultured under N-, P- and Fe-limited growth trial. Thus, while the near-infrared spectroscopy technique has potential for assessment of dry matter and lipid content of microalgal cultures using a dried filtrate sample, further work is required to examine the limits to model robustness.

Rapid Determination of Chemical Oxygen Demand in Sugar Refinery Wastewater by Short-Wave Near-Infrared Spectroscopy

Near-infrared (NIR) spectroscopy combined with partial least squares (PLS) regression and Savitzky-Golay (SG) smoothing was successfully applied for the rapid no-reagent determination of chemical oxygen demand (COD) in sugar refinery wastewater. Among 141 samples, 60 were randomly selected as the validation set. The remaining 81 samples were divided into the calibration set (50 samples) and the prediction set (31 samples) for a total of 20 times with certain similarities. The results showed that the short-wave NIR region (780 nm to 1100 nm) can be used as information waveband of COD in sugar refinery wastewater. The optimal SG smoothing parameters and PLS factors OD, DP, NSP, and F were 5, 5, 7, and 6, respectively. The modeling effects M-SEPAve, M-RP,Ave, M-SEPStd, and M-RP,Stdwere 24.3 mg/L, 0.971, 2.7 mg/L, and 0.007, respectively. The validation effects V-SEP and V-RP were 25.9 mg/L and 0.963, respectively. The results indicated that the method has good prediction effect and stability, and provided a reliable analysis model and valuable references for designing specialized instruments.

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.