Short-wavelength Near-infrared Research Articles

Comparison between consumer-oriented and laboratory benchtop near-infrared spectrometers for total soluble solids measurement in grape

Portable near-infrared (NIR) spectrometers are gaining interest as a non-destructive tool for fruit quality determination in the decade. In this study, the performance of the portable NIR spectrometer for TSS prediction of grapes was evaluated and compared to the benchtop spectrometer. A total of 105 table grapes were individually measured NIR spectra at short-wavelength (740 – 1070 nm) and long-wavelength NIR region (1000 – 2500 nm) using SCiO and NIRFlex N-500 spectrometers, respectively. Partial least square (PLS) regression analysis was performed on berry spectra from both devices afterward significant wavelengths were identified and used to develop multiple linear regression (MLR) models. The best PLS prediction model was obtained from SNV pretreating spectra for both devices and spectral data acquired from SCiO showed better prediction performance (= 0.854, SEP = 0.452°Brix) than NIRFlex N-500 spectra (= 0.667, SEP = 0.675°Brix). Low predicting ability was gained for the MLR calibration model for both device with an RPD of about 1.70. From the results, a pocket-size spectrometer has the potential to be a non-destructive sorting and screening tool in fruit industries.

Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy

The maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using a completely non-destructive measurement. Both NIR spectrometers were investigated: the short wavelength NIR (SWNIR) ranging from 450 to 1000 nm and long wavelength NIR (LWNIR) ranging from 860 to 1750 nm. The samples collected for experimentation consisted of four stages: immaturity, prematurity, maturity, and ripe. Each fruit was scanned at the rind position on the main fertile lobe (header, middle, and tail) and stem. The classification models were developed using three supervised machine learning algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and K-Nearest neighbours (KNN). The analysis results revealed that the use of durian rind spectra only obtained between 83.15% and 88.04% accuracy for the LWNIR spectrometer, while the SWNIR spectrometer provided 64.73 to 93.77% accuracy. The performance of model increases when developing with combination between rind and stem spectra. The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency, exhibiting 97.28% and 100% accuracy for LWNIR and SWNIR spectrometers, respectively. The LDA model is therefore recommended for obtaining spectra from smoothing moving average (MA) + baseline of rind position and when used in combination with the MA + standard normal variance (SNV) of stem spectra. The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage. This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.

A unified heuristic approach to simultaneously detect fusarium and ergot damage in wheat

Fusarium spp. and Ergot (Claviceps purpurea) are fungal pathogenic species that infect cereals worldwide, causing widespread economic losses. Fusarium-damaged kernels (FDKs) in wheat often contain harmful mycotoxins, while Ergot damage (ERG) is commonly characterized by the presence of alkaloids. Both FDK and ERG damage could cause health hazards to people and animals. Currently, assessment of FDKs and ERG is performed by human visual inspection using distinctly different biochemical assays and/or wet chemistry methods, all of which are subjective, time-consuming and non-repeatable. In this study, the authors present a unified non-destructive optical method using visible-near-infrared (VNIR) (400-1000 nm) and short-wavelength near-infrared (SWIR) (1000-2500 nm) hyperspectral imaging (HI) to simultaneously detect FDK and ERG damage in wheat kernels. The un-mixing technique of spectral angle mapping (SAM) was employed for pixel- and object-wise classification of the three classes i.e. FDK, ERG and control (healthy), of non-touching wheat kernels. Overall classification accuracies of VNIR–93% and SWIR–85% were obtained with 100% classification of ERG samples in the VNIR range. To reduce model complexity, principle component analysis (PCA) was explored, and a subset of 12 significant wavelengths was selected. The SAM model based on these selected wavelengths did not achieve accurate prediction, indicating it requires the entire spectral data for reliable performance. However, a partial least square discriminant analysis (PLSDA) model using the reduced wavelength subset achieved overall classification accuracies of VNIR–94% and SWIR–90%, which were better when compared to the SAM method. This study is the first known attempt to simultaneously identify two important fungal diseases in wheat using a completely non-invasive and non-destructive method. The results confirm the feasibility of developing a fast and cost-effective multi-spectral classification technique for FDK and ERG detection.

Aquaphotomics for monitoring of groundwater using short-wavelength near-infrared spectroscopy

Water spectrum of any aqueous system contains information about OH covalent and hydrogen bonds that are highly influenced by the environment and the rest of the molecules in the system. When aquaphotomics is used to analyze the water near infrared (NIR) spectra, the information about the water molecular structure can be obtained as a function of internal and external factors.The objective of this research is to apply aquaphotomics analysis to evaluate different groundwaters by using their NIR unique spectral pattern, robust to external influences of temperature and humidity, that can potentially be used for water type identification and screening practice. Two groundwaters obtained at different depths and their mixture, differing in mineral content and molecular structure were monitored on a daily basis using portable visible/NIR (vis/NIR) spectrometer during three consecutive years. The spectra were pre-processed by smoothing and multiplicative scatter correction (MSC) to remove noise and baseline effects. Results showed that NIR spectral patterns of groundwater samples were affected by changes in environmental factors – temperature, humidity, time and others. The water absorbance bands which are highly influenced by humidity and temperature in short wavelength NIR region were identified. Their avoidance resulted in obtaining consistent spectral patterns during the entire monitoring period, unique for each groundwater, that can be used as its fingerprint and monitored over time.Consistency and uniqueness of the spectral pattern for each groundwater provide a potential to use the deviation of spectral pattern as an indicator of changes in the water. These results confirm that vis/NIR spectral pattern can be used as an integrative marker of water status, stable over time, providing the basis for an efficient cost-effective method for monitoring of water functionality.

Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties. The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

Grouping marian plums harvested at different times by transmittance near-infrared spectroscopy

Marian plum (Bouea burmanica Griff.) 'Toon Klaow' is one of Thailand's favorite fruits. Marian plum's edible quality depends strongly on its harvest time. This study investigated a non-destructive technique for classifying marian plums according to their harvest time after the day that their blossom set. The non-destructive technique used was transmittance mode, short wavelength near-infrared (SW-NIR) spectroscopy in the wavelength range 660-960 nm. Marian plum samples (n=110) were harvested at 62, 65, 68 and 73 days after flowering. The soluble solids content (SSC) and titratable acid (TA) were determined accurately by standard methods. SW-NIR spectra of these samples were obtained and analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The following spectral pretreatments were applied, standard normal variate (SNV), smoothing (Savitsky-Golay), and first derivative, in order to obtain optimal grouping results. The PC1 and PC2 score plot of the PCA could not clearly separate some of the classified groups. For the results of PLS-DA, its cross-validated grouping accuracy was R=0.91 and RMSECV=1.28; hence, it can be concluded that SW-NIR spectroscopy has good potential for determining the harvest time of marian plums.

Design of polymer multilayer heterostructure broadband reflector for the near-infrared using genetic algorithm

A broadband reflector for the near-infrared (NIR) was designed using a multilayer heterostructure consisting of several quarter-wave stacks (QWSs), which were composed of polymethyl methacrylate (PMMA) and polyethylene terephthalate (PET) films. Taking the solar power density as the target and using the Bragg wavelength of each QWS as the variable, the genetic algorithm was applied to look for the optimal multilayer heterostructure for broadband NIR reflection. As high as 99.46% total energy reflectivity in the short-wavelength NIR region (780 to 1100 nm) and 89.56% total energy transmissivity in the visible light region (380 to 780 nm) were realized by a multilayer heterostructure consisting of six quarter-wave PMMA/PET stacks, which can be easily fabricated based on the micronano multilayer coextrusion technology. The designed structure possesses good stability, and its total energy reflectivity is not sensitive to the incident angle of light. The proposed broadband NIR reflector can be applied to buildings as energy-saving films.

Design of broadband near-infrared reflector using polymer multilayer heterostructure with low-refractive-index contrast

In the paper, to achieve total reflection in the short-wavelength near-infrared (NIR) region (780–1100 nm), a broadband NIR reflector using polymer multilayer heterostructure composed of several quarter-wave stacks was proposed. The reflector used two kinds of polymer materials: PMMA and PET with low-refractive-index contrast. Taking the solar power density as the target and the Bragg wavelength of each quarter-wave stack as the variable, a searching method based on arithmetic progression was applied to find the optimal results for broadband NIR reflection. The simulation results show that the broadband NIR reflector can obtain higher total reflectivity with the increase of the number of quarter-wave stacks, and 99.06% total reflectivity in the short-wavelength NIR region can be achieved by the polymer multilayer heterostructure with six quarter-wave stacks. The proposed polymer multilayer heterostructure can be fabricated by the micro–nanomultilayer co-extrusion technology based on torsion lamination, and could be applied in agriculture as NIR-blocking films.

Performance of NIR handheld spectrometers for the detection of counterfeit tablets

Near Infrared (NIR) spectroscopy is an attractive tool for pharmaceutical analyses. While lab spectrometers are very performant, they are expensive and due to their size, not adapted for field analyses. In this study, two handheld NIR spectrometers have been evaluated for the fast detection of counterfeits of pharmaceutical tablets: one low cost sensor providing a short wavelength NIR range (swNIR) and one handheld spectrometer providing a classical NIR range (cNIR).A large database containing almost all the tablets produced by the firm was created on each spectrometer. A screening for supervised classifications was performed in order to determine the most accurate model for product authentication. A Support Vector Machine (SVM) model was finally chosen for the swNIR, providing 100% of correct identification in calibration and 96.0% in validation, and a Linear Discriminant Analysis (LDA) model was chosen for the cNIR delivering 99.9% of correct identification in calibration and 91.1% in validation. Challenging samples (counterfeits and generics) could be 100% identified by the chosen classifiers combined with a class name check and a correlation distance. Statistical tests were used to compare the performance of selected swNIR and cNIR models. These results demonstrate that both devices can be used for tablet identification and the detection of counterfeits.

Identification of informative bands in the short-wavelength NIR region for non-invasive blood glucose measurement.

The "glucose-linked wavelength" in the short-wavelength near-infrared (NIR) region, in which the light intensity reflected from the hand palm exhibits a good correlation to the blood glucose value, was investigated. We performed 391 2-h carbohydrate tolerance tests (CTTs) using 34 participants and a glucose-linked wavelength was successfully observed in almost every CTT; however, this wavelength varied between CTTs even for the same person. The large resulting data set revealed the distribution of the informative wavelength. The blood glucose values were efficiently estimated by a simple linear regression with clinically acceptable accuracies. The result suggested the potential for constructing a personalized low-invasive blood glucose sensor using short-wavelength NIR spectroscopy.

Forecast of Apple Internal Quality Indices at Harvest and During Storage by VIS-NIR Spectroscopy

Harvest date is generally established by monitoring small batches of fruit prior to harvest for changes in maturity parameters such as firmness, starch index, and total soluble solid content using destructive methods. Substituting this method with nondestructive visible-near infrared (VIS-NIR) spectrophotometers could save manpower hours and improve accuracy for harvesting for prolonged storage. Six hundred apples (Malusdomestica Borkh.) from three different orchards for each of three cultivars, “Granny Smith,” “Pink Lady,” and “Starking” were used to build calibration and validation models in different spectral regions. Two instruments, VIS-NIR (340–1,014 nm) and short-wavelength near-infrared (SWIR) (850–1,888 nm) spectrophotometers, measured the apples at harvest both in a static position and on a moving cell conveyer, and these measurements were used to predict total soluble solid (TSS) content, titratable acidity (TA), and firmness at harvest and after 2, 4, and 6 months of 0 °C storage. Starch was also predicted at harvest. The best R 2 values were for TSS and starch (0.86 to 0.91) while TA and firmness predictions were less precise (0.53 to 0.78). The findings of the study indicate that the method offers potential for nondestructive prediction of ripeness and quality parameters of different cultivars of apples originating from different orchards. Moreover, the method enables forecasting of apple internal composition changes during storage based on the spectral signature at the time of harvest. Application of the results of this study could serve as a basis for the development of an automatic system for forecasting of apple internal composition change and of a sorting system.

NONDESTRUCTIVE PREDICTION OF INTERNAL BROWNING IN PINEAPPLE USING TRANSMITTANCE SHORT WAVELENGTH NEAR INFRARED SPECTROSCOPY

ISHS Southeast Asia Symposium on Quality Management in Postharvest Systems and Asia Pacific Symposium on Postharvest Quality Management of Root and Tuber Crops NONDESTRUCTIVE PREDICTION OF INTERNAL BROWNING IN PINEAPPLE USING TRANSMITTANCE SHORT WAVELENGTH NEAR INFRARED SPECTROSCOPY

An alternative approach for the classification of orange varieties based on near infrared spectroscopy

A multivariate technique and feasibility of using near infrared spectroscopy (NIRS) for non-destructive discriminating Thai orange varieties were studied in this paper. Short-wavelength near infrared (SW-NIR) spectra in region of 643 to 970nm were collected from 100 orange sample of each varieties. A total of 300 spectra were used to develop an accurate classification model by diversity of classifiers. The result showed that Logistic Regression (LGR) model was achieved 100% classification accuracy while Multi-Criteria Quadratic Programming (MCQP) and Support Vector Machine (SVM) ones also demonstrated satisfying result (95%). In order to find simpler and easier interpretable classification model, several feature selection techniques were evaluated to identify the most relevant wavelengths to the orange varieties. With four principal components (PCs) from Principal Component Analysis (PCA) and the effective wavelengths of 769.68, 692.28, 662.61 and 959.31nm from Least Square Forward Selection (LS-FS), the reduced classification models of LGR also achieved satisfying classification accuracy respectively. Furthermore, both Kernel Principal Component Analysis (KPCA) and Kernel Least Square Forward Selection (KLS-FS) with SVM enhanced performance of models by 5 PCs and features respectively. The result concluded that NIRS can yield an accurate classification for Thai tangerine varieties from whole spectra and can enhance interpretability of classification model by feature subset.

Rapid noninvasive characterization of ovarian follicular atresia in cultured white sturgeon ( Acipenser transmontanus) by near infrared spectroscopy

We report a rapid and noninvasive method based on short wavelength near infrared (SW-NIR) spectroscopy to detect onset of atresia in farmed white sturgeon ( Acipenser transmontanus) (N = 10). The only current means to assess follicular atresia is by direct oocyte examination which requires a surgical biopsy of the ovary. In this study, abdominal scans were collected noninvasively by SW-NIR on anesthetized females using a diffuse reflectance fiber optic probe. In addition, to further verify the ability to detect spectroscopical changes related to onset of atresia, during each sampling roughly 30 cm 3 of ovarian follicles was surgically removed from each female and transferred to a Teflon holder for spectral acquisition. Comparison of spectra collected on normal or atretic fish or ovarian follicles was conducted using Principal Component Analysis (PCA). Principal components suggest that the best indicator of atresia onset is a decrease in the intensity of the lipid bands at 930 nm. Prediction models for atresia were constructed using Soft Independent Modeling of Class Analogy (SIMCA) with leave-one-out cross validation. Seventy one percent of all atretic spectra and 65% of normal spectra collected on ovarian follicles were correctly classified. Exclusion of spectra from two potential outlier fish improved the predictability of normal ovarian follicles to 76%. Similarly, 72% of all atretic spectra and 59% of all normal spectra collected noninvasively in whole fish were correctly classified. Exclusion from the model of spectra from the same two outlier fish improved prediction of atresia from 72% to 75% as well as improving the prediction of normal spectra from 59% to 62%. This study represents the first example of using a noninvasive approach based on SW-NIR to detect onset of atresia in female sturgeon. Upon further development, this approach may potentially replace the need for surgical biopsy to detect ovarian regression. The availability of the proposed spectroscopic approach would grant sturgeon growers a powerful tool to follow more closely the maturation cycle with the goal of producing a consistently uniform product, standardize processing conditions, and maximize caviar yield by harvesting fish when the ovarian follicles have the appropriate firmness and a larger size.

Nondestructive assessment of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy

A technique was developed to predict the freshness of packaged sliced chicken breast employing a nondestructive visible and short-wavelength near-infrared (SW-NIR) spectroscopy method. Spectra were recorded at 0, 7 and 14days using a camera, spectral filter (400–1000nm) and a halogen flood lighting system which were developed and calibrated for the purpose. Physicochemical, biochemical and microbiological properties such as moisture (xw), water activity (aw), pH, total volatile basic nitrogen (TVB-N), ATP breakdown compounds (K1 values) and mesophilic bacteria (cfu g−1) were determined to predict freshness degradation. The spectra obtained were related to the storage time of the samples. The best wavelengths for modeling freshness were 413, 426, 449, 460, 473, 480, 499, 638, 942, 946, 967, 970 and 982nm. A linear correlation was found between the visible and SW-NIR spectroscopy and parameters such as microbiological counts, K1 and T-VBN indexes.

A noninvasive near infrared system for detection of platelet components contaminated with bacteria

Platelet (PLT) transfusion-associated bacterial sepsis has remained a substantial patient risk, primarily due to lacking effective and point-of-issue measures to detect bacterial contamination. This study describes near infrared (NIR) spectroscopy to examine inoculated PLTs without sampling within a few seconds. This study evaluated apheresis PLTs inoculated with low concentrations of Bacillus cereus and Staphylococcus epidermidis, comparing with sterile bags. Short-wavelength NIR spectra over the range from 700 to 1100 nm in the transmittance mode were obtained using research (NIRS6500, Foss NIRSystems) and portable (NIRGun, Shizuoka Shibuya Seiki) instruments at 6-hour intervals from 0 to 72 hours after inoculation (HAI). The sensitivity of the NIRS6500 was 100% (43/43) and 98% (50/51) after incubating PLTs inoculated with B. cereus for 42 HAI or more and with S. epidermidis for 54 HAI or more, respectively. Difference spectra calculated by subtracting the NIR spectra of stored PLTs with that of the same PLTs measured at 0 HAI improved the discrimination results compared with conventional second derivative spectra. The NIRS6500 system can provide a PLT monitoring system based on difference spectra. The chemical components of PLTs that were influenced by bacterial metabolism seemed to play an important role in the calibration structure. Further studies should examine samples spiked with various species of prevalent bacteria.

Artificial neural networks for non-destructive determination of acetylspiramycin powder by short-wavelength NIR spectroscopy

The present study has aimed at providing new insight into short-wavelength near-infrared (SW-NIR) spectroscopy (780–1100 nm) for non-destructive quantitative analysis of acetylspiramycin (macrolide antibiotics) powder by using artificial neural networks (ANNs). Presently, it was shown the third vibrational overtone of the C H stretching band can be used to quantitatively determine constituents in pharmaceutical. The third overtone referred to as the SW-NIR region ranges from 780 nm to 1100 nm. In this paper, 156 experimental samples of acetylspiramycin powder were analyzed using ANNs in the 780–1100 nm region of SW-NIR spectra. Four different pretreated methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to three sets of SW-NIR spectra of powder samples. The results presented here demonstrate that the SW-NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis. Degree of approximation as an evaluation criterion of the network was employed, which proved the accuracy of the predicted results.

Application of artificial neural networks to the nondestructive determination of ciprofloxacin hydrochloride in powder by short-wavelength NIR spectroscopy

The present study is aimed at providing a new short-wavelength near-infrared (NIR) spectroscopic method for the nondestructive quantitative analysis of ciprofloxacin hydrochloride in powder via artificial neural networks (ANNs). For this purpose, the NIR spectra of 90 experimental powder samples in the range 700–1100 mm were analyzed. Four different pretreatment methods—first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC)—were applied to three sets of the NIR spectra of the powder samples. Among all of the ANN models, the first-derivative model is found to be the best. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of the major components in pharmaceuticals. The degree of approximation as an evaluation criterion prevents the overfitting phenomenon occurring in ANNs.