Soil Spectroscopy Evolution: A Review of Homemade Sensors, Benchtop Systems, and Mobile Instruments Coupled with Machine Learning Algorithms in Soil Diagnosis for Precision Agriculture.

In precision agriculture, soil spectroscopy has become an invaluable tool for rapid, low-cost, and nondestructive diagnostic approaches. Various instrument configurations are utilized to obtain spectral data over a range of wavelengths, such as homemade sensors, benchtop systems, and mobile instruments. These data are then modeled using a variety of calibration algorithms, including Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machines (SVM), these datasets are further improved and optimized. Given the increasing demand for cost-effective and portable solutions, homemade sensors and mobile instruments have gained popularity in recent years. This review paper assesses the current state of soil spectroscopy by comparing the performance, accuracy, precision, and applicability of homemade sensors, mobile spectrometers, and traditional benchtop instruments. The discussion encompasses the technological advancements in homemade sensors, exploring innovative approaches taken by researchers and farmers, as well as developing affordable and efficient soil spectroscopy tools. Mobile and benchtop spectrometers, equipped with cutting-edge technology, have enabled easy soil diagnosis, transforming the landscape of soil analysis.

Soil Particle Size Thresholds in Soil Spectroscopy and Its Effect on the Multivariate Models for the Analysis of Soil Properties.

This study focused on one of the few but critical sample preparations required in soil spectroscopy (i.e., grinding), as well as the effect of soil particle size on the FTIR spectral database and the partial least squares regression models for the prediction of eight soil properties (viz., TC, TN, OC, sand, silt, clay, Olsen P, and CEC). Fifty soil samples from three Moroccan region were used. The soil samples underwent three preparations (drying, grinding, sieving) to obtain, at the end of the sample preparation step, three ranges of particle size, samples with sizes < 500 µm, samples with sizes < 250 µm, and a third range with particles < 125 µm. The multivariate models (PLSR) were set up based on the FTIR spectra recorded on the different obtained samples. The correlation coefficient (R2) and the root mean squared error of cross validation (RMSECV) were chosen as figures of merit to assess the quality of the prediction models. The results showed a general trend in improving the R2 as the finer particles were used (from <500 µm to 125 µm), which was clearly observed for TC, TN, P2O5, and CEC, whereas the cross-validation errors (RMSECV) showed an opposite trend. This confirmed that fine soil grinding improved the accuracy of predictive models for soil properties diagnosis in soil spectroscopy.

Binational survey using Mytilus galloprovincialis as a bioindicator of microplastic pollution: Insights into chemical analysis and potential risk on humans.

Microplastic (MP) contamination in edible mussels has raised concerns due to their potential risk to human health. Aiming to provide valuable insights regarding the occurrence, physicochemical characteristics, and human health implications of MP contamination, in the present study, two nationwide surveys of MP contamination in mussels (Mytilus galloprovincialis) were conducted in Morocco and Tunisia. The results indicated that MP frequency ranged from 79 % to 100 % in all the analyzed samples. The highest MP density was detected in mussels from Morocco (gills "GI": 1.88 MPs/g ww-1; digestive glands "DG": 0.92 MPs/g ww-1) compared to mussels of Tunisia (GI: 1.47 MPs g- 1; DG: 0.79 MPs g- 1). No significant differences in MP density were found between the two organs (GI and DG) for both countries. MPs were predominantly blue and black fibers, and smaller than 1000 µm. Seven polymeric types were identified, of which PET, PP, and PE were the most abundant, accounting for >87 % of all samples. Scanning Electron Microscopy (SEM) coupled with Energy dispersive X-ray (EDX) showed that most MPs have noticeable signs of weathering and inorganic components on their surface. The highest MP daily intake was found in children, while the lowest was estimated in women and men. Moreover, the annual dietary exposure of MPs through mussel consumption was estimated to be 1262.17 MPs/year in Morocco and 78.18 MPs/year in Tunisia. The potential risk assessment of MPs in mussels based on the polymer hazard index (PHI) was estimated in the high-risk levels, implying that MPs may pose health risks to humans. Overall, this research suggests that the consumption of mussels represents a considerable MP exposure route for the Moroccan and Tunisian populations.

Comparison of green bio-based cerium/alginate vs. copper/alginate beads: a study of vibrational and thermal properties using experimental and theoretical methods.

Herein, bio-based alginates (Alg) containing metallic beads (Ce and Cu) were synthesized via an alginate cross-linking method, and their properties were studied using experimental techniques combined with theoretical simulations. Materials were characterized through Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and scanning electron microscope (SEM) images, to determine the cross-linking structural features, thermal stability, and surface morphology of alginates. Besides, density functional theory (DFT) methods were employed to calculate global reactivity parameters such as HOMO-LUMO gap energies (ΔEH-L), electronegativity (χ), hardness (η), and electrophilic and nucleophilic indicators, using both gas and aqueous media for the study of the complexation process. Among other features, characterization of the thermal properties showed that Alg@Ce and Alg@Cu alginate beads behave differently as a function of the temperature. This behavior was also predicted by the conformation energy differences between Alg@Ce and Alg@Cu, which were found out theoretically and explained with the combined study of the vibrational modes between the carboxylate group with either Ce or Cu. Overall, the reactivity of the Alg@Ce alginate bead was higher than that of the Alg@Cu counterpart, results could be used as a cornerstone to employed the materials here studied in a wide range of applications.

Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy.

Vibrational spectroscopy such as Fourier-transform infrared (FTIR), has been used successfully for soil diagnosis owing to its low cost, minimal sample preparation, non-destructive nature, and reliable results. This study aimed at optimizing one of the essential settings during the acquisition of FTIR spectra (viz. Scans number) using the standardized moment distance index (SMDI) as a metric that could trap the fine points of the curve and extract optimal spectral fingerprints of the sample. Furthermore, it can be used successfully to assess the spectra resemblance. The study revealed that beyond 50 scans the similarity of the acquisitions has been remarkably improved. Subsequently, the effect of the number of scans on the predictive ability of partial least squares regression models for the estimation of five selected soil properties (i.e., soil pH in water, soil organic carbon, total nitrogen, cation exchange capacity and Olsen phosphorus) was assessed, and the results showed a general tendency in improving the correlation coefficient (R2) as the number of scans increased from 10 to 80. In contrast, the cross-validation error RMSECV decreased with increasing scan number, reflecting an improvement of the predictive quality of the calibration models with an increasing number of scans.

New insights into the Argan oil categories characterization: Chemical descriptors, FTIR fingerprints, and chemometric approaches.

The characterization of Argan oils to classify them in three categories ('Extra Virgin', 'Virgin' and 'Lower quality') was evaluated. A total of 120 Moroccan Argan oils samples from the Taroudant Argan forest was investigated. The free acidity, peroxide value, spectrophotometric indices (K232 and K270), fatty acids, sterols, and tocopherol contents were assessed. The samples were also scanned by FTIR spectroscopy. The Principal Component Analysis (PCA) and four classification methods, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modelling of Class Analogy (SIMCA), K-nearest Neighbors (KNN), and Support Vector Machines (SVM), were applied on both the chemical and spectral data. Besides the conventional chemical profiling, FTIR spectra were evaluated for their feasibility as a rapid non-invasive approach for classifying and predicting the oil quality categories. The most important variables for differentiating the oil categories were identified as K232, peroxide value, É£-tocopherol, δ-tocopherol, acidity, stigma-8-22-dien-3ß-ol, stearic acid (C18:0) and linoleic acid (C18:2) and could be used as quality indicators. Eight chemical descriptors or key features from the FTIR spectra (selected by interval-PLS) could also be established as indicators of quality and freshness of Argan oils.

Classification of polymorphic forms of fluconazole in pharmaceuticals by FT-IR and FT-NIR spectroscopy.

The main goal of this work was to test the ability of vibrational spectroscopy techniques to differentiate between different polymorphic forms of fluconazole in pharmaceutical products. These are mostly manufactured with fluconazole as polymorphic form II and form III. These crystalline forms may undergo polymorphic transition during the manufacturing process or storage conditions. Therefore, it is important to have a method to monitor these changes to ensure the stability and efficacy of the drug. Each of FT-IR or FT-NIR spectra were associated to partial least squares-discriminant analysis (PLS-DA) for building classification models to distinguish between form II, form III and monohydrate form. The results has shown that combining either FT-IR or FT-NIR to PLS-DA has a high efficiency to classify various fluconazole polymorphs, with a high sensitivity and specificity. Finally, the selectivity of the PLS-DA models was tested by analyzing separately each of three following samples by FT-IR and FT-NIR: lactose monohydrate, which is an excipient mostly used for manufacturing fluconazole pharmaceutical products, itraconazole and miconazole. These two last compounds mimic potential contaminants and belong to the same class as fluconazole. Based on the plots of Hotelling's T² vs Q residuals, pure compounds of miconazole and itraconazole, that were analyzed separately, were significantly considered outliers and rejected. Furthermore, binary mixtures consist of fluconazole form-II and monohydrate form with different ratios were used to test the suitability of each technique FT-IR and FT-NIR with PLS-DA to detect minimum contaminant or polymorphic conversion from a polymorphic form to another using also the plots of Hotelling's T² vs Q residuals.

Discrimination of diesel fuels marketed in Morocco using FTIR, GC-MS analysis and chemometrics methods.

The purpose of this study was to perform a discrimination and classification of diesel samples from the four major suppliers of petroleum products in Morocco using Fourier Transform Infrared Spectroscopy (FTIR), Gas Chromatography coupled with Mass Spectrometry (GC-MS) and chemometrics tools. Eighty diesel samples were collected from different gas stations owned by the four biggest brands in the Moroccan market. Principal Component Analysis (PCA) was performed to depict the similarities between the samples and check the presence of outliers. Partial Least Squares Discriminant Analysis (PLS-DA) models were set up for the discrimination and the classification of the four groups of samples (i.e., diesel suppliers). The models proposed in this study, were characterized by good prediction abilities, especially the FTIR-PLSDA model that was characterized by 100% of accurate discrimination of the four groups. The approach of analysis showed that the FTIR spectra can provide a cheap and rapid means for the determination of the diesel origin and to ensure the traceability of diesel products marketed in Morocco with respect for the rules of the green chemistry.

Discrimination and Quantification of Moroccan Gasoline Adulteration with Diesel Using Fourier Transform Infrared Spectroscopy and Chemometric Tools.

In this work, transform-infrared spectroscopy (FTIR) was associated with chemometric tools, especially principal component analysis (PCA) and partial least squares regression (PLSR), to discriminate and quantify gasoline adulteration with diesel. The method is composed of a total of 100 mixtures were prepared, and then FTIR fingerprints were recorded for all samples. PCA was used to verify that mixtures can be distinguished from pure products and to check that there are no outliers. As a result of using just PC1 and PC2, more than 98% of the general variability was explained. The PLSR model based on infrared spectra has shown its capabilities to be suitable for predicting gasoline adulteration in the concentration range of 0 to 98% (w/w), with a high significant coefficient of determination (R² = 99.25%) and an acceptable calibration and prediction errors (root mean squared error of calibration = 0.63 and root mean square of external validation and/or prediction = 0.69).