Common Latent Space Exploration for Calibration Transfer across Hyperspectral Imaging-Based Phenotyping Systems

Hyperspectral imaging has increasingly been used in high-throughput plant phenotyping systems. Rapid advancement in the field of phenotyping has resulted in a wide array of hyperspectral imaging systems. However, sharing the plant feature prediction models between different phenotyping facilities becomes challenging due to the differences in imaging environments and imaging sensors. Calibration transfer between imaging facilities is crucially important to cope with such changes. Spectral space adjustment methods including direct standardization (DS), its variants (PDS, DPDS) and spectral scale transformation (SST) require the standard samples to be imaged in different facilities. However, in real-world scenarios, imaging the standard samples is practically unattractive. Therefore, in this study, we presented three methods (TCA, c-PCA, and di-PLSR) to transfer the calibration models without requiring the standard samples. In order to compare the performance of proposed approaches, maize plants were imaged in two greenhouse-based HTPP systems using two pushbroom-style hyperspectral cameras covering the visible near-infrared range. We tested the proposed methods to transfer nitrogen content (N) and relative water content (RWC) calibration models. The results showed that prediction R2 increased by up to 14.50% and 42.20%, while the reduction in RMSEv was up to 74.49% and 76.72% for RWC and N, respectively. The di-PLSR achieved the best results for almost all the datasets included in this study, with TCA being second. The performance of c-PCA was not at par with the di-PLSR and TCA. Our results showed that the di-PLSR helped to recover the performance of RWC, and N models plummeted due to the differences originating from new imaging systems (sensor type, spectrograph, lens system, spatial resolution, spectral resolution, field of view, bit-depth, frame rate, and exposure time) or lighting conditions. The proposed approaches can alleviate the requirement of developing a new calibration model for a new phenotyping facility or to resort to the spectral space adjustment using the standard samples.

Evaluation of Kraft pulp yield and syringyl/guaiacyl ratio from standing trees (Eucalyptus camaldulensis, E. urophylla, Leucaena leucocephala and Casuarina junghuhniana) using portable near infrared spectroscopy

Measurement of pulpwood traits from a standing tree has considerable advantage when screening large populations for tree selection. It reduces time and also eliminates requirements of transport, powdering, and storing the sample. This study describes estimation of Kraft pulp yield (KPY) in Eucalyptus camaldulensis, E. urophylla, Leucaena leucocephala, and Casuarina junghuhniana by portable NIR spectroscopy of standing trees. Calibration models were developed for KPY estimation using portable NIR spectroscopy for the four species, along with a calibration model for syringyl/guaiacyl (S/G) ratio in E. camaldulensis. The calibration models for KPY showed R2 values ranging from 0.93 ( E. camaldulensis) to 0.83 ( L. leucocephala), and 0.95 for S/G ratio. The developed calibration models for E. camaldulensis and L. leucocephala were compared with laboratory NIR models, and a variation of <±2.0% was found between both methods. The models were validated by both external and cross validation which showed <2.0% RMSEP (root mean square error of prediction) and <2.0% RMECV (root mean square error of cross validation) in external and cross validations, respectively.

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

INVESTIGATION OF THE POSSIBILITY OF USING THE METHOD OF DIFFERENTIAL SPECTROPHOTOMETRY FOR THE ANALYSIS OF OVERLAPPING ABSORPTION BANDS OF TRITERPENE SAPONINS CHROMOPHORES AND THEIR QUANTITATIVE DETERMINATION IN AQUEOUS SOLUTIONS

A detailed analysis of the absorption spectra of mono- and bidesmoside triterpene glycosides was performed. It is suggested that the maximum in the region of 198–208 nm is attributed to the absorption of the double bond in the cyclohexene ring of the aglycone. The second less seen maximum at a wavelength of 280 nm is observed in the spectrum of saponin Quillaja Saponaria and is almost completely absent in the spectrum of saponin Mukorossi Sapindus. Identification of this maximum is extremely difficult, since its presence can be due to both the aldehyde group in the aglycone and the hydroxyl and carboxyl groups in the carbohydrate molecules. Using the method of differential spectrophotometry, a method for decomposing the UV spectrum of saponins into its constituent components is proposed and justified. A spectral analysis was performed, including the assignment of the absorption bands to the functional groups of the studied compounds. The possibility of estimating changes in the spectral properties of glycoside solutions with changes in the acidity of the medium is considered. The possibility of a bathochromic shift of the maximum absorption of the aglyconic part of saponin depending on the microenvironment (the structure of the carbohydrate part) and changes in the pH of the solution is established. It is shown that the bands at 274, 280.5 nm correspond to n-π* transitions of carbonyl and carboxyl groups and are determined only in the regions of high concentrations in Quillaja Saponaria saponin solutions. Based on the results obtained, calibration models for the quantitative determination of saponins in solutions are proposed. The regression analysis of the calibration equations is carried out, the main statistical indicators are calculated.

Raman Calibration Models for Chemical Species Determination in CO2-Loaded Aqueous MEA Solutions Using PLS and ANN Techniques

The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO2-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO2-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R2) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R2 values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot.

Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics

Tea is a widely consumed drink in the world which is susceptible to undergoing adulterations to reduce manufacturing costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, as well as to detect and quantify frauds, is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify, and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety and, except for some white tea extracts, perfectly discriminated from the chicory ones. One hundred percent classification rates for the PLS-DA calibration models were achieved, except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, also showing, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available.

Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy

Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover (n = 156) and wheat straw (n = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, n = 126; wheat straw, n = 108) and the remaining set used for validation (corn stover, n = 30; wheat straw, n = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration (n = 233) and validation (n = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQC) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQC values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQC = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.