The rising demand for chitin and chitosan in chemical, agro-food, and healthcare industries is creating a need for rapid and high-throughput analysis. The physicochemical properties of these biopolymers are greatly dependent on the degree of acetylation (DA). Conventional methods for DA determination, such as LC-MS and ¹H NMR, are time-consuming when performed on many samples, and therefore efficient methods are needed. Here, high-throughput microplate-based FTIR and FT-Raman methods were compared with their manual counterparts. Partial least squares regression models were based on 30 samples of chitin and chitosan with reference DA values obtained by LC-MS and ¹H NMR, and the models were validated on an independent test set of 16 samples. The overall predictive accuracy of the high-throughput methods was at the same level as the manual methods and the well-established LC-MS and ¹H NMR methods. Therefore, high-throughput FTIR and FT-Raman DA determination methods have great potential to serve as fast and economical substitutes for traditional methods.

Core Ideas: Conventional methods for measuring CEC are time consuming and costly. A Vis – NIRS model and pedotransfer function (PTF) were developed for CEC determination. The Vis – NIRS model estimated CEC accurately for different soil types. The Vis – NIRS CEC model performed better than the PTF based on clay and organic C contents. Knowledge of the cation exchange capacity (CEC) for soils or other porous media is very important for civil engineering and agricultural applications. However, the standard laboratory methods to measure CEC are costly and laborious. The aim of this research was to develop a visible–near‐infrared spectroscopy (Vis–NIRS, 400–2500 nm) calibration model to predict CEC based on multivariate analysis and to compare the predictive ability of Vis–NIRS with that of a pedotransfer function (PTF). For this purpose, reference CEC was measured by the ammonium acetate method for 235 soil samples, collected from 21 countries. Diffuse spectral reflectance data were also collected by using a NIRSTM DS2500 spectrometer. The model was constructed on a calibration subset (80%) and evaluated with a validation subset (20%) using partial least squares regression. The Vis–NIRS calibration model was sufficiently robust based on the cross‐validation results [ R ² = 0.79, RMSE of cross‐validation values of 7.9 cmol _c kg ⁻¹ and bias = −0.14]. The independent validation of the Vis–NIRS model showed good prediction accuracy, regardless of sample origin (RMSE of prediction value of 5.0 cmol _c kg ⁻¹ and ratio of performance to interquartile distance value of 4.5). Moreover, the Vis–NIRS prediction performance was superior to that of the PTF, which was influenced by the sample origin (RMSE values of 11.5 cmol _c kg ⁻¹ ). The better prediction of CEC by the Vis–NIRS calibration model suggests that it is due to the co‐variation of CEC with clay (type and content) and organic C content.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.

The Atterberg limits (plastic limit [PL] and liquid limit [LL]) of fine‐grained soils are important for civil engineering and agronomic applications. Several methodologies exist to determine the PL and LL, each with associated merits and shortcomings. The reproducibility and uncertainties associated with the various methods have not been evaluated for different samples that differ in terms of clay mineralogy and geologic origin. The objectives were to (i) assess the repeatability of two methods each for determining the PL (thread rolling and rolling device) and LL (Casagrande cup and drop cone penetrometer), and compare the subsequent plasticity indexes for 30 samples from 10 countries; and (ii) evaluate the effect of particle size (2 mm and 425 µm) on the plastic and liquid limits for 28 Venezuelan samples. For the PL, the repeatability (estimated as the repeatability coefficient, RC, among replications) for the thread rolling and rolling device methods was similar (2.28 and 2.39%, respectively). For the LL, the drop cone method was 16% more repeatable than the Casagrande cup method. There was a strong correlation between the two respective methods for PL ( r ² = 0.98) and LL ( r ² = 0.99). For samples that have LL < 45, the LL from the Casagrande cup was slightly higher than LL from the drop cone, and vice versa for samples with LL > 45%. No significant differences were observed between PL measured on <2‐mm or <425‐µm samples; for samples with LL < 35% the two particle sizes gave similar values. There were however larger discrepancies between the two particle sizes for samples with LL > 36%.

Core Ideas: Two methods each to measure each Atterberg limit (SL, PL, and LL) were evaluated. A Vis‐NIRS technique successfully estimated Atterberg limits for fine‐grained soils. The Atterberg limits correlated with typical clay signatures in the Vis‐NIR spectrum Vis‐NIRS model performance was independent of the method used to generate the reference data. The Atterberg limits (shrinkage limit [SL], plastic limit [PL], and liquid limit [LL]) describe the physico‐mechanical behavior of soils and thus are crucial for civil and agricultural applications. Conventional laboratory methods for measurement of these limits are tedious and costly for a large number of samples. Our objective was to develop visible–near‐infrared spectroscopy (Vis‐NIRS, from 400–2500 nm) based reliable models to estimate the Atterberg limits. Two conventional methods for each Atterberg limit were used to generate the reference data: paraffin wax and Hg methods for SL; rolling and motorized devices for PL; and Casagrande cup and drop‐cone penetrometer methods for LL. Calibration models were built on 80% of the data using partial least squares regression and validated with the remaining 20% of the dataset. The Vis‐NIRS independent validation of LL showed very good estimation with standardized RMSE (SRMSE = RMSE/Range) of 0.16 and 0.15, respectively, for LL _drop‐cone and LL _Casagrande methods. Similarly for PL, the Vis‐NIRS estimation accuracy was quite good with SRMSE values of 0.18 and 0.22, respectively, for PL _motorized and PL _rolling . Reasonably good estimation was obtained for the SL _paraffin and SL _Hg with SRMSE of 0.25 for both methods. The results suggest that the Vis‐NIRS calibration models and the accuracy following validation were similar for the pair of methods used for the SL, PL, and LL. Finally, analyses of the model regression coefficients revealed that the important wavelengths to estimate the SL, PL, and LL in the Vis‐NIR regions were present across the entire Vis‐NIR spectrum and were strongly related to clay type and content.