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.

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.

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.

Enhanced quantification of chlorophyll a and its degradation products in olive oil using time-resolved laser-induced fluorescence fingerprint analysis.

Potential errors in the fluorescence analysis of chlorophylls and their degradation products, primarily due to spectral overlap and inner filter, are widely acknowledged. This study aimed to devise a sensitivity-enhanced technique for the concurrent quantification of chlorophyll a and its degradation products while minimizing effects from type-B chlorophylls. Initially, a time-resolved laser-induced fluorescence spectroscopic system was designed and tested on stardard chlorophyll samples. The origins, implications, and mitigation strategies of spectral overlap and the inner filter effect on the measured fluorescence intensity were thoroughly examined. Then, this methodology was proved to be efficacious within complex liquid matrices derived from olive oil. The experimental outcomes not only shed additional light on the mechanisms of chlorophyll fluorescence overlap and the inner filter effect, but also establish a general framework for developing spectrally and timely resolved fluorescence fingerprint analysis for the simultaneous quantification of chlorophylls and their degradation products at high concentrations.

Evaluating Soluble Solids in White Strawberries: A Comparative Analysis of Vis-NIR and NIR Spectroscopy.

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible-near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500-978 nm) and NIR (908-1676 nm) data collected from 180 samples of "Tochigi iW1 go" white strawberries and 150 samples of "Tochigi i27 go" red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R2p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R2p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system.

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.

Analysis of the Pomelo Peel Essential Oils at Different Storage Durations Using a Visible and Near-Infrared Spectroscopic on Intact Fruit.

Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography-mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, ß-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, ß-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage.

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.

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.

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.

Monitoring of hydroxyl groups in wood during heat treatment using NIR spectroscopy.

This paper deals with the evaluation of thermally treated wood by near-infrared (NIR) spectroscopy. In the NIR second derivative spectrum, the absorption band at 6913 cm(-1) appeared with the procession of heat treatment, which conclusively assigned to the phenolic hydroxyl groups due to the lignin in comparison with the spectrum of acetylated spruce wood. As a result of the changes in the ratio of the areal integral calculated from spectral separation in the region of hydroxyl groups (7200-6100 cm(-1)) by the Gauss-Newton method, it was clear that the degradation of hydroxyl group in the cellulose started predominantly from the amorphous region and followed to semicrystalline and crystalline region. There was an obvious correlation between the weight decrement of wood and the decrement of hydroxyl groups in the cellulose by heat treatment.

Near-infrared spectroscopic monitoring of the water adsorption/desorption process in modern and archaeological wood.

We investigated the adsorption/desorption mechanism of water and the variation of water adsorption for modern and archaeological wood using near-infrared spectroscopy. A mixture model of water was used to decompose the near-infrared difference spectra into three components (free water molecules (S 0), those with one OH group engaged in hydrogen bonding (S1), and those with two OH groups engaged in hydrogen bonding (S2)) based on a principal component analysis. The variations of each water component with relative humidity could be explained by proposing a model that describes water absorption in three stages. It was concluded that the aging phenomenon in wood is due to the decrease of adsorption sites on hemicellulose and amorphous cellulose.

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.

Estimation of wood stiffness and strength properties of hybrid larch by near-infrared spectroscopy.

This work was undertaken to investigate the feasibility of near-infrared (NIR) spectroscopy for estimating wood mechanical properties, i.e., modulus of elasticity (MOE) and modulus of rupture (MOR) in bending tests. Two sample sets having large and limited density variation were prepared to examine the effects of wood density on estimation of MOE and MOR by the NIR technique. Partial least squares (PLS) analysis was employed and it was found that the relationships between laboratory-measured and NIR-predicted values were good in the case of sample sets having large density variation. MOE could be estimated even when density variation in the sample set was limited. It was concluded that absorption bands due to the OH group in the semi-crystalline or crystalline regions of cellulose strongly influenced the calibrations for bending stiffness of hybrid larch. This was also suggested from the result that both alpha-cellulose content and cellulose crystallinity showed moderate positive correlation to wood stiffness.

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.