Effect of dielectric barrier discharge (DBD) plasma treatment on physicochemical and 3D printing properties of wheat starch.

In recent years, the focus has shifted towards carbohydrate-based hydrogels and their eco-friendly preparation methods. This study involved an investigation into the treatment of wheat starch using dielectric barrier discharge (DBD) plasma technology over varying time gradients (0, 2, 5, 10, 15, and 20 min). The objective was to systematically examine the impact of different treatment durations on the physicochemical properties of wheat starch and the suitability of its gels for 3D printing. Morphology of wheat starch remained intact after DBD treatment. However, it led to a reduction in the amylose content, molecular weight, and crystallinity. This subsequently resulted in a decrease in the pasting temperature and viscosity. Moreover, the gels of the DBD-treated starch exhibited superior 3D printing performance. After a 2-min DBD treatment, the 3D printed samples of the wheat starch gel showed no significant improvements, as broken bars were evident on the surface of the 3D printed graphic, whereas DBD-20 showed better printing accuracy and surface structure, compared to the original starch without slumping. These results suggested that DBD technology holds potential for developing new starch-based gels with impressive 3D printing properties.

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data.

Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80-88% TSI, and more than 88% TSI. The 80-88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

Validation study on light scattering changes in kiwifruit during postharvest storage using time-resolved transmittance spectroscopy.

Visible and near-infrared spectroscopy has been well studied for characterizing the organic compounds in fruit and vegetables from pre-harvest to late harvest. However, due to the challenge of decoupling of optical properties, the relationship between the collected samples' spectral data and their properties, especially their mechanical properties (e.g., firmness, hardness, and resilience) is hard to understand. This study developed a time-resolved transmittance spectroscopic method to validate the light scattering changing characteristics in kiwifruit during shelf-life and in cold storage conditions. The experimental results demonstrated that the reduced scattering coefficient ([Formula: see text]) of 846 nm inside kiwifruit decreased steadily during postharvest storage and is more evident under shelf-life than in cold storage conditions. Moreover, the correlation between the [Formula: see text] and the storage time was confirmed to be much higher than that using the external color indexes measured using a conventional colorimeter. Furthermore, employing time-resolved profiles at this single wavelength, an efficacious mathematical model has been successfully formulated to classify the stages of kiwifruit softening, specifically early, mid-, and late stages. Notably, classification accuracies of 84% and 78% were achieved for the shelf-life and cold storage conditions, respectively.

Visualization of Sugar Content Distribution of White Strawberry by Near-Infrared Hyperspectral Imaging.

In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913-2166 nm) is developed. NIR-HSI data collected from 180 samples of "Tochigi iW1 go" white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and R2p values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries.

Application of near-infrared spectroscopy to agriculture and forestry.

Depending on the uniformity of the quality attribute within agricultural products, there is often a need to develop non-destructive and efficient evaluation methods to assure their qualities. Near-infrared spectroscopy (NIRS) is a well-suited method to characterize organic compounds, particularly when coupled with multivariate analysis methods. This review article introduces scientific and technical reports using the NIRS to evaluate food, agriculture, and forest products. Overall, basic spectroscopic research is continuously progressing; indeed, in combination with big-data information technology and spectral imaging techniques, material analysis is improving to maximize performance. Portable and low-cost devices have also been designed and produced, enabling remote analysis. Future advancements are expected to result in its applications in even more fields for online or at-line quality monitoring.

Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks.

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system. Several nondestructive automatic identification systems have been developed, but there is room for improvement to construct a highly reliable model. The present study proposes cognitive spectroscopy that combines near infrared hyperspectral imaging (NIR-HSI) with a deep convolutional neural network approach. We defined "cognitive spectroscopy" as a protocol that extracts features from complex spectroscopic data and presents the best results without human intervention. Overall, 120 samples representing 38 hardwood species were scanned using an NIR-HSI camera. A deep learning prediction model was built based on the principal component (PC) images obtained from the PC scores of hyperspectral images (wavelength range: 1000-2200 nm at approximately 6.2 nm interval). The results showed that the accuracy of wood species identification based on 6PC (PC1-PC6) images was 90.5%, which was considerably higher than the accuracy of 56.0% obtained with conventional visible images.

High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging.

Nitrate is an important component of the nitrogen cycle and is therefore present in all plants. However, excessive nitrogen fertilization results in a high nitrate content in vegetables, which is unhealthy for humans. Understanding the spatial distribution of nitrate in leaves is beneficial for improving nitrogen assimilation efficiency and reducing its content in vegetables. In this study, near-infrared (NIR) hyperspectral imaging was used for the non-destructive and effective evaluation of nitrate content in spinach (Spinacia oleracea L.) leaves. Leaf samples with different nitrate contents were collected under various fertilization conditions, and reference data were obtained using reflectometer apparatus RQflex 10. Partial least squares regression analysis revealed that there was a high correlation between the reference data and NIR spectra (r2 = 0.74, root mean squared error of cross-validation = 710.16 mg/kg). Furthermore, the nitrate content in spinach leaves was successfully mapped at a high spatial resolution, clearly displaying its distribution in the petiole, vein, and blade. Finally, the mapping results demonstrated dynamic changes in the nitrate content in intact leaf samples under different storage conditions, showing the value of this non-destructive tool for future analyses of the nitrate content in vegetables.

The effect of path length, light intensity and co-added time on the detection limit associated with NIR spectroscopy of potassium hydrogen phthalate in aqueous solution.

Near infrared (NIR) spectroscopy is a common means of non-invasively determining the concentrations of organic compounds in relatively transparent aqueous solutions. Rigorous determination for limit of detection (LOD) is of importance for the application use of NIR spectroscopy. The work reported herein determined the LOD with the analysis of potassium hydrogen phthalate (KHP) in water with partial least square (PLS) calibration in the range of 6300-5800 cm-1 between the two strong absorption bands of water, in which the C-H overtone bands of KHP are located. A comparison of the LOD estimated when using various condition (path length, aperture and co-added scan times) showed that the lowest LOD for KHP obtained with a fiber optic cable attachment equipped NIR spectrometer is approximately 150 ppm.

Optical properties of drying wood studied by time-resolved near-infrared spectroscopy.

We measured the optical properties of drying wood with the moisture contents ranging from 10% to 200%. By using time-resolved near-infrared spectroscopy, the reduced scattering coefficient µs' and absorption coefficient µa were determined independent of each other, providing information on the chemical and structural changes, respectively, of wood on the nanometer scale. Scattering from dry pores dominated, which allowed us to determine the drying process of large pores during the period of constant drying rate, and the drying process of smaller pores during the period of decreasing drying rate. The surface layer and interior of the wood exhibit different moisture states, which affect the scattering properties of the wood.

Determination of true optical absorption and scattering coefficient of wooden cell wall substance by time-of-flight near infrared spectroscopy.

The true absorption coefficient (µa) and reduced scattering coefficient (µ´s) of the cell wall substance in Douglas fir were determined using time-of-flight near infrared spectroscopy. Samples were saturated with hexane, toluene or quinolone to minimize the multiple reflections of light on the boundary between pore-cell wall substance in wood. µ´s exhibited its minimum value when the wood was saturated with toluene because the refractive index of toluene is close to that of the wood cell wall substance. The optical parameters of the wood cell wall substance calculated were µa = 0.030 mm(-1) and µ´s= 18.4 mm(-1). Monte Carlo simulations using these values were in good agreement with the measured time-resolved transmittance profiles.

Mapping of leaf water content using near-infrared hyperspectral imaging.

In this study, near-infrared hyperspectral imaging was applied to predict the water content of golden pothos (Epipremnum aureum) leaves, after which partial least squares regression (PLSR) analysis was performed to predict their averaged water content. The resulting predictive model was then applied to each single-pixel spectra in order to construct a water content image that could be used to evaluate the model's applicability to the single-pixel spectra through partial least squares score comparisons between the averaged spectra used for calibration and the single-pixel spectra. In the next phase, it was determined that a rebuilt PLSR predictive model based on the averaged spectra of an applicable pixel showed higher prediction accuracy than that of the original model. This study provides effective information about the limitations of prediction mapping and the optimization of pixel selections for better calibrations.

Time-resolved principal component imaging analysis of chlorophyll fluorescence induction for monitoring leaf water stress.

Chlorophyll fluorescence induction is widely applied to investigate plant growth conditions by calculating the ratio of its intensity at oxidized and reduced states. We examined the applicability of a time-resolved profile of chlorophyll fluorescence induction with the aid of multivariate analysis to monitor the leaf water stress. Principal component (PC) analysis of time-resolved images of chlorophyll fluorescence induction and their score images were reconstructed. Control leaves (non-stressed leaves) and water-stressed leaves could be classified by normalized PC3 score images. This technique has the potential to monitor the water stress condition of plants by using a simple device.

Rapid prediction of past climate condition from lake sediments by near-infrared (NIR) spectroscopy.

This study explored the feasibility of rapid, nondestructive near-infrared (NIR) reflection spectroscopy for the prediction of conventional physical properties, carbon-nitrogen-sulfur (CNS) analysis, and concentration of inorganic components in sediment cores from a brackish lake. A long core sample, which consisted of well-preserved annually formed lamina from Lake Ogawara along the Pacific coast in Aomori Prefecture, northeastern Japan, was used to investigate the past environmental record. The core was previously analyzed for physical properties, CNS, and inorganic components. Calibration models were developed from NIR reflection spectra of 149 core samples. Partial least squares (PLS) analysis provided good regression models between measured and predicted values for water content, total nitrogen (TN), total organic carbon (TOC), total sulfur (TS), Al(2)O(3), S/Al(2)O(3), Fe(2)O(3)/Al(2)O(3), Sc/Al(2)O(3), Cu/Al(2)O(3), and Zn/Al(2)O(3) with coefficients of determination (r(2)) for cross-validation of 0.73, 0.89, 0.88, 0.73, 0.92, 0.81, 0.82, 0.75, 0.82, and 0.82, respectively. The variation of predicted component values as a function of depth showed the same trend as that of conventionally measured values. This study also showed the possibility of NIR spectroscopy as an on-site, rapid analytical tool for the identification of tephra (fragmental material produced by a volcanic eruption regardless of composition, fragment size, or emplacement mechanism), which is important for dating.

Difference of the crystal structure of cellulose in wood after hydrothermal and aging degradation: a NIR spectroscopy and XRD study.

The change of crystalline structure in hydrothermally treated hinoki wood was investigated by means of Fourier-transform near-infrared spectroscopy in combination with a deuterium exchange method and X-ray diffraction. The results were compared with analogous data of dry-exposed archeological wood taken from an old wooden temple. Although the decomposition of the amorphous regions in cellulose and hemicelluloses, which corresponds to an increase of the degree of crystallinity, was observed for both, archeologically and hydrothermally treated wood, the increase of crystallite thickness was confirmed only for hydrothermally treated wood. The increase of the average size of crystallites corresponds well to the measured decrease of the deuteration accessibility of the crystalline regions. As the accessibility of the crystalline regions decreased for both, D(2)O and t-butanol, it is assumed that due to the expansion of the crystalline domains by hydrothermal treatment several elementary fibrils are arranged at distances below 0.3 nm.

Near-infrared spectroscopic assessment of contamination level of sewage.

We examined the use of near infrared (NIR) spectroscopy as a rapid technique for the evaluation of sewage quality. Influent water samples, primary sedimentation tank water samples, and final effluent water samples were collected from sewage treatment facilities in Nagoya, Japan and their NIR spectra obtained. Partial least squares (PLS) models for total phosphate (TP), total nitrogen (TN), biochemical oxygen demand (BOD), total organic carbon (TOC), and turbidity of sewage water were constructed from the NIR data. The models provided good correlation between measurements obtained conventionally and those predicted from spectroscopy. Spectral variation induced by background interference in samples affected accuracy. Loading plots and score plots derived from PLS regression analysis resolved the background interference and allowed highly accurate predictions. Spectral variation induced by contamination in the sewage was a main predictor of sewage quality. These results show that NIR spectroscopy shows potential for in-line, non-destructive measurement of sewage quality.

Feasibility of near-infrared spectroscopy for on-line grading of sawn lumber.

Near-infrared (NIR) spectroscopy, coupled with multivariate analysis, has been used to evaluate the wood properties of sawn lumber of Japanese larch (Larix kaempferi), whose diffuse reflection spectra were acquired under static and moving conditions. Prediction models of the dynamic modulus of elasticity (E(fr)), the modulus of elasticity in bending tests (E(b)), the bending strength (F(b)), the wood density (DEN), and the moisture content (MC) were developed using partial least squares (PLS) analysis. For all wood properties, models obtained from data collected under the moving condition as an analogue of on-line measurement were superior to those from the static condition data. The regression coefficients for the PLS models predicting the mechanical properties in both static and moving conditions showed clear peaks at the absorption bands due to the three major polymers of wood, i.e., cellulose, hemicellulose, and lignin. NIR spectroscopy has high potential for the on-line grading of sawn lumber.

Near-infrared spectroscopic investigation of the hydrothermal degradation mechanism of wood as an analogue of archaeological wood. Part II: hardwood.

Near-infrared (NIR) spectroscopy and chemometrics were applied to analyze the degradation mechanism of hardwood following hydrothermal treatment. NIR spectra, chemical composition, oven-dried density, equilibrium moisture content, compressive Young's modulus parallel to grain, and cellulose crystallinity of artificially degraded beech as an analogue of archaeological wood were systematically measured. Partial least squares (PLS) regression analysis was employed to predict compressive Young's modulus using NIR spectra and various properties as independent variables. Results are also compared with previous data obtained from similar treatment of softwood (Hinoki cypress). The increase in cellulose crystallinity of hardwood during the initial stage of hydrothermal treatment (up to 5 hours) was correlated with an improvement in the mechanical properties of wood. Young's modulus for both hardwood and softwood showed a gradual decrease over five hours of hydrothermal treatment, which is proposed to be due to the degradation of polysaccharide.

Application of time-of-flight near-infrared spectroscopy to fruits: analysis of absorption and scattering conditions of near-infrared radiation using cross-correlation of the time-resolved profile.

The absorption and scattering conditions of near-infrared radiation in a grapefruit, a popular thick-peeled fruit, were investigated by time-of-flight near-infrared spectroscopy (TOF-NIRS). The cross-correlation function was introduced to obtain fine spectroscopic information from the time-resolved profile. Variation of the optical parameters in both the time-resolved profile and the cross-correlation function showed that the NIR radiation was largely absorbed in the peel and considerably scattered in the flesh of the fruit. It also reflected the straightness of the input pulsed laser. The substantial optical path length of the grapefruit estimated from the cross-correlation function was approximately 4 to 5 times as long as the nominal optical path length (NOPL). The cross-correlation function was an effective tool to analyze the absorption/scattering conditions of NIR radiation in a sample where an unstable light source such as a Nd:YAG laser with high output energy was employed.

Near-infrared spectroscopic investigation of the hydrothermal degradation mechanism of wood as an analogue of archaeological objects. Part I: softwood.

The degradation mechanism of softwood due to the variation of strength was analyzed in conjunction with spectroscopy and chemometrics, where the sample was thermally treated with a steam atmosphere. Near-infrared (NIR) spectra, chemical composition, oven-dried density, equilibrium moisture content, compressive Young's modulus parallel to the grain, and cellulose crystallinity of artificially degraded hinoki cypresses as an analogue of archaeological objects were systematically measured. Partial least squares (PLS) regression analysis was employed to predict compressive Young's modulus using NIR spectra and some kinds of wood properties as independent variables. Good prediction models were obtained for both independent variables. The scores and the loading plots derived from PLS analysis were applied to consistently explain the mechanism of hydrothermal degradation. It was suggested that the variation of compressive Young's modulus with hydrothermal treatment was governed by two main components, that is, depolymerization of polysaccharides and variation of cellulose crystallinity.

Discriminant analyzing system for wood wastes using a visible-near-infrared chemometric imaging technique.

A new optical system was developed and applied to automated separation of wood wastes, using a combined technique of visible-near-infrared (Vis-NIR) imaging analysis and chemometrics. Three kinds of typical wood wastes were used, i.e., non-treated, impregnated, and plastic-film overlaid wood. The classification model based on soft independent modeling of class analogy (SIMCA) was examined using the difference luminance brightness of a sample. Our newly developed system showed a good/promising performance in separation of wood wastes, with an average rate of correct separation of 89%. Hence, it is concluded that the system is efficiently feasible for online monitoring and separation of wood wastes in recycling mills.