Root Mean Square Error Of Prediction Research Articles

Accurate analysis of coal calorific value using NIRS-XRF: Utilizing RF classification and PLSR subtype modeling

Rapid and precise measurement of the calorific value of coal is crucial for coal chemical enterprises. However, due to the wide variety of coal sources and the diverse types of coal, the matrix effect significantly reduces the accuracy of spectroscopic rapid detection techniques for coal quality. To address this issue, this study conducts research on a coal calorific value detection method based on the combined use of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF), exploring the feasibility of identifying coal types first and then predicting calorific value based on corresponding subtype models. In the aspect of spectral preprocessing, fusion of the dual spectra is achieved using techniques such as truncation, smoothing, Standard Normal Variate (SNV), and normalization. For coal type identification, the identification performance of five machine learning algorithms is compared, and the random forest (RF) algorithm is used to improve the identification accuracy to 98.43 %. In terms of prediction modeling, a comparison is made between traditional holistic models based on Partial Least Squares Regression (PLSR) and subtype models built according to coal types, revealing that the latter demonstrating stronger generalization ability and robustness. Accordingly, a precise prediction strategy for coal calorific value based on the combined use of NIRS and XRF in complex coal types is proposed: first, the RF algorithm is used to automatically identify coal types based on the fusion spectrum of NIRS and XRF, and then the calorific value of the corresponding coal type is accurately predicted using the PLSR subtype model selected according to the identification results. Industrial test results indicate that the average absolute error (AAE), root mean square error of prediction (RMSEP), average relative error (ARE), and standard deviation (SD) for coal calorific value prediction are 0.20 MJ/kg, 0.24 MJ/kg, 0.79 %, and 0.04 MJ/kg, respectively, meeting the requirements of enterprises. The research findings provide a practical technical method for timely and accurate acquisition of coal calorific value data, addressing the interference problem of matrix effects in complex coal type scenarios and holding broad application prospects in the coal-based energy industries.

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

Near-infrared spectroscopy in resin transfer molding—determination of the degree of cure

AbstractNear-infrared (NIR) spectroscopy is employed to directly monitor the degree of cure of composite material during the curing phase in the resin transfer molding (RTM) process. The composite material consists of natural fibers and an epoxy-amine resin system. The quantitative determination of the degree of cure, α, from the NIR spectra is realized by partial least square (PLS) regression in conjunction with various pre-treatments of the spectral data. As a reference, the degree of cure is determined by isothermal differential scanning calorimetry (DSC) experiments. The best PLS model that could be obtained for α is characterized by a determination coefficient of prediction (R²P) of 0.980 and root mean square error of prediction (RMSEP) of 4.4 %. While the spectra change during curing according to the literature for each RTM trial, significant differences can be observed between the spectra of different RTM trials. The reasons for this are discussed in detail. The findings show the potential of inline NIR spectroscopy for monitoring α in the RTM process.

Rapid determination of residual pefloxacin in mutton based on hyperspectral imaging and data fusion

Non-destructive detection of veterinary drug residues in meat products is critical for ensuring food safety and human health. The feasibility of using visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging (HSI) system combined with data fusion for the prediction of pefloxacin residues in mutton was investigated in this study. A partial least squares regression optimization (PLSR) model was developed by multivariate data processing methods. The results showed that the low-level fusion (LLF) produced better results with R2P = 0.907 and root mean square error of prediction (RMSEP) = 0.462 as compared to individual data blocks. Furthermore, intermediate-level fusion (ILF) based on iterative retention of informative variables (IRIV) algorithm showed the best results with R2P = 0.940 and RMSEP = 0.375. Finally, the visualized distributions of Vis-NIR and NIR based on the optimal model combination were mapped. The results have demonstrated that a combination of HSI with data fusion may be applied to rapidly and non-destructively detect pefloxacin residues in mutton.

Automated and non-destructive estimation of soluble solid content of tomatoes on the plant under variable light conditions

In this study, a method is proposed to deal with the variable light conditions in a greenhouse to non-destructively predict the soluble solid content (SSC) of tomatoes on the plant. It was investigated how well the SSC – measured as °Brix – of tomatoes could be predicted based on spectral data in the range of 470–900 nm, where data acquired in situ (in the greenhouse) was compared to post-harvest data captured under controlled laboratory conditions. To deal with the variation in illumination in the greenhouse, a dynamic-calibration method is proposed, using a grey reference in the image. Ground-truth SSC data of the tomatoes was acquired using a refractometer. Data was collected of three different types of truss tomatoes with a wide range of SSC. Different PLS regression models were then trained on the spectral data in relation to the refractometer values. Trained and tested on all types, the in situ measurements showed a predicted coefficient of determination on the test set, Q2, of 0.95 with a Root Mean Squared Error of Prediction (RMSEP) of 0.29 °Brix using the dynamic calibration, and a Q2 of 0.93 with RMSEP of 0.35 °Brix without using the dynamic-calibration method. The post-harvest measurements resulted in a Q2 of 0.95 with RMSEP of 0.31 °Brix. The results show that spectral imaging using dynamic calibration is applicable for in situ non-destructive prediction of SSC. This method enables high-throughput and non-destructive quality estimation of fruits on the plant in commercial greenhouse conditions.

Abbreviated Urine Collection Compared With 24-Hour Urine Collection for Measuring Creatinine Clearance in Adult Critically Ill Patients: A Systematic Review.

To evaluate the accuracy of abbreviated urine collection (≤12 hours) compared with 24-hour urine collection for measuring creatinine clearance (CrCl) in critically ill adult patients. We searched PubMed, Embase, Web of Science, Google Scholar, and ProQuest Dissertations and Thesis Global; screened reference lists of included studies; and contacted the authors when needed. English studies only were considered with no restriction on dates. After duplicate removal, 2 reviewers screened titles/abstracts, reviewed full-text articles, and extracted data independently. Studies that compared abbreviated versus 24-hour urine collection for measuring CrCl were included. We assessed the risk of bias using the QUADAS-2 tool. We extracted correlation coefficients, mean prediction errors (ME)-as a measure of bias, and root mean squared prediction errors (RMSE)-as a measure of precision. Five studies were included, comprising 528 adult critically ill adults from surgical, medical, and trauma intensive care units (ICUs). Three studies had high risk of bias, and 2 had low risk. The studies evaluated different durations of urine collection, including 30-minute, 2-hour, 4-hour, 6-hour, and 12-hour. Mean 24-hour CrCl ranged from 57 mL/min/1.73 m2 to 103 mL/min. Abbreviated urine collection led to CrCl that correlated well with the 24-hour measured CrCl (correlation coefficient ranged from 0.8 to 0.95). Mean prediction error ranged from 5 mL/min/1.73 m2 to 16 mL/min (from 8% to 25% of the 24-hour CrCl). Root mean squared prediction error calculated from 1 study was 30.5 mL/min/1.73 m2. Abbreviated urine collection is used to measure CrCl for renal drug dosing in critically ill patients, but its accuracy is not well-established. Abbreviated urine collection may overestimate CrCl compared with 24-hour urine collection. Larger, well-conducted studies are needed to evaluate the accuracy of CrCl measured using different durations of urine collection in critically ill patients.

Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis

This study addresses the challenge of effectively selecting relevant variables and providing interpretable insights in spectroscopic analysis (1100–2498 nm) of corn quality (moisture, fat, protein, and starch), incorporating variable selection techniques and explainable artificial intelligence (AI). Three variable selection algorithms were used to select 36 important variables, and through combinatorial optimization, only 11 common wavelengths were chosen. Partial least squares regression (PLSR) models were developed using the full spectral range, individual variables, and common variables. Based on the root mean square errors of prediction (RMSEP), the PLSR models with common variables outperformed individual feature variables for starch (0.22 % vs. 0.24 %) and were comparable for oil (0.05 % vs. 0.05 %), water (0.02 % vs. 0.01 %) and protein (0.12 % vs. 0.09 %). The SHapley Additive exPlanation (SHAP) method was employed to explain the PLSR model and assess the contribution of common variables. This combined approach significantly improved the accuracy and interpretability of corn prediction models, providing trust in the results and facilitating the understanding of the relationship between spectral features and corn quality attributes.

Predicting fat cover in beef cattle to make on-farm management decisions: a review of assessing fat and of modeling fat deposition

Abstract Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: (1) conduct a brief review of the biological basis of adipose tissue accretion, (2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, (3) review fat deposition models: Davis growth model (DGM), French National Institute for Agricultural Research growth model (IGM), Cornell Value Discovery System (CVDS), and BeefSpecs drafting tool (BeefSpecsDT) and (4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 days in a commercial feedlot. Evaluating the observed and predicted fat mass (FM) is the focus of this review. The FM mean bias (MB) for Salers heifers were 7.5 and 1.3kg and the root mean square error of prediction (RMSEP) were 31.2 and 27.8kg and for Angus-Hereford steers the MB were -4.0 and -10.5kg and the RMSEP were 9.14 and 21.5kg for DGM and IGM, respectively. The FM MB for Angus steers were -5.61 and -2.93kg and the RMSEP were 12.3 and 13.4kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21, 12, and 68% and 5, 4, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20kg level of tolerance 91 and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of: (1) striving to minimize inputs, and (2) choosing on-farm inputs that are readily available.

Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor’s performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor’s efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

Soybean polypeptide content detection based on fusion of spectral and image information

Soybean polypeptide is a protein hydrolysate. It has multiple benefits, such as anticancer, antioxidant, etc. Addressing the limitations of traditional methods, such as time-consuming procedures and high costs, this study proposed a novel approach for the rapid, nondestructive detection of soybean polypeptide content. This approach utilizes the fusion of spectral and image information. First, seven preprocessing methods were applied to the spectral data. The results indicated that the model accuracy was highest after applying the standard normal variate preprocessing method. Second, a combined method, consisting of synergy interval partial least squares and iteratively retains informative variables, was proposed to extract characteristic variables. A two-layer Stacking framework ensemble learning model was constructed to compare the detection effects between single spectral input and fusion of spectral and image information input. The results showed that the fusion input model achieved the best performance. The coefficient of determination (R 2) of prediction of the fusion input model reached 0.952, an improvement of 0.061 compared to single spectral input. And root mean square error of prediction (RMSEP) of the fusion input model reached 1.803 × 10−4, a reduction of 7.43 × 10−5 compared to single spectral input. These results indicate that the fusion of spectral and image information can effectively improve the model’s accuracy, offering valuable technical support for soybean breeding and quality detection.

Pulpwood holocellulose model transfer based on screening with consistent and stable signals algorithm.

In this study, in order to realize the sharing of the near-infrared analysis model of holocellulose between three spectral instruments of the same type, 84 pulp samples and their content of holocellulose were taken as the research objects. The effects of 10 pre-processing methods, such as 1st derivative (D1st), 2nd derivative (D2nd), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), autoscaling, normalization, mean centering and pairwise combination, on the transfer effect of the stable wavelength selected by screening wavelengths with consistent and stable signals (SWCSS) were discussed. The results showed that the model established by the wavelength selected by the SWCSS algorithm after the autoscaling pre-processing method had the best analysis effect on the two target samples. Root mean square error of prediction (RMSEP) decreased from 2.4769 and 2.3119 before the model transfer to 1.2563 and 1.2384, respectively. Compared with the full-spectrum model, the value of AIC decreased from 3209.83 to 942.82. Therefore, the autoscaling pre-processing method combined with SWCSS algorithm can significantly improve the accuracy and efficiency of model transfer and provide help for the application of SWCSS algorithm in the rapid determination of pulp properties by near-infrared spectroscopy (NIRS).

Optimization of watermelon waste as a bulking agent for sustainable co-composting of livestock manures using response surface methodology

Global human population growth has resulted in significant intensive agricultural activity, posing substantial challenges to waste management and environmental conservation. Watermelon waste (WW), chicken manure (CM) and horse manure (HM) are among the main contributors to agricultural waste due to their abundant waste production. This study aims to manage the daily production of these wastes by utilizing WW as a bulking agent in the co-composting of CM and HM. Response surface methodology (RSM) was employed to analyze the effects of four independent factors: HM:CM composition, particle size, composting period, and bulking agent amount. Thirty treatments were developed using central composite design and in-vessel composting reactors were employed to study the relationship between the factors involved and compost physicochemical quality parameters. The results demonstrated significant effects on organic matter (OM), nitrogen (N), potassium (K), dry matter, moisture content, bulk density, and pH, while the carbon-to-nitrogen ratio (C/N) and phosphorus level were not affected. The optimized co-composting conditions obtained from RSM were 75:25 for HM:CM composition (%), 0.5 cm for particle size, 40 days for composting period, and 10% of WW, resulting in a compost with 61% OM, 2.5% N, and 2.5% K. These optimal conditions agreed closely with the predicted values; root mean square prediction error (RMSPE) was less than 0.50, revealing the success of RSM in determining optimal process parameters and developing models for predicting responses. Our study demonstrated that WW as a bulking agent in the co-composting of CM and HM has significantly enhanced the organic matter and nutrient levels of the final compost product.

Three-dimensional imaging to estimate in vivo body and carcass chemical composition of growing beef-on-dairy crossbred bulls

The dynamics of cattle body chemical composition during growth and fattening periods determine animal performance and beef carcass quality. The aim of this study was to estimate the empty body (EB) and carcass chemical composition of growing beef-on-dairy crossbred bulls (Brown Swiss breed as dam with Angus, Limousin or Simmental as sire) using three-dimensional (3D) imaging. The 3D images of the cattle’s external body shape were recorded in vivo on 48 bulls along growth trajectory (75–520 kg BW and 34–306 kg hot carcass weight [HCW]; set 1) and on 70 bulls at target market slaughter weight, including 18 animals from set 1 (average 517 ± 10 kg BW and 289 ± 10 kg HCW; set 2). The linear, circumference, curve, surface and volume measurements on the 3D body shape were determined. Those predictive variables were used in partial least square regressions, together with the effect of the sire breed whenever significant (P < 0.05), with leave-one-out cross-validation to estimate water, lipid, protein, mineral and energy mass or proportions in the EB and carcass. Mass and proportions were determined directly from post-mortem grinding and chemical analyses (set 1) or indirectly using the 11th rib dissection method (set 2). In set 1, bulls’ BW and HCW were estimated via 3D imaging, with root mean square error of prediction (RMSEP) of 12 kg and 6 kg, respectively. The EB and carcass chemical component proportions were estimated with RMSEP from 0.2% for EB minerals (observed mean 3.7 ± 0.2%) to 1.8% for EB lipid (11.6 ± 4.2%), close to the RMSEP found for the carcass. In set 2, the RMSEP for estimation via 3D imaging was 9 kg for BW and 6 kg for HCW. The EB energy and protein proportions were estimated, with RMSEP of 0.5 MJ/kg fresh matter (10.1 ± 0.8 MJ/DM) and 0.2% (18.7 ± 0.7%), respectively. Overall, the estimations of chemical component proportions from 3D imaging were slightly less precise for both sets than the mass estimations. The morphological traits from the 3D images appeared to be precise estimators of BW, HCW as well as EB and carcass chemical component masses and proportions.

[Hologram quantitative structure-activity relationship on the gas chromatographic retention index of plant essential oil constituents].

The gas chromatography retention index (RI) is an important parameter for the identification of different types of compounds in the field of chromatographic analysis; however, the experimental collection of RI values is a extremely cumbersome process. Thus, there is an urgent need for the establishment of a simple, efficient, and accurate model for the prediction of the RI values of compounds. In this study, first, the experimental RI values for 60 plant essential oil constituents were obtained. Next, a model describing the hologram quantitative structure-activity relationship (HQSAR) between the structural properties of the essential oil constituents and their RI values was investigated and constructed. The optimal HQSAR model was established by setting the model parameters "fragment size", "fragment distinction", "hologram length" and "principal components" to "1-4", "C, Ch", "199", and "4", respectively. Finally, the predictive ability of the model was verified using external test set validation and leave-one-out cross-validation (LOO-CV). The experimental results were as follows, the root mean square error of prediction (RMSEP), predictive determination coefficient ([Formula: see text]), concordance correlation coefficient (CCC), and mean relative error (MRE) for external test set validation were 40.45, 0.984, 0.968, and 2.20%, respectively. Meanwhile, the root mean square error of cross validation (RMSECV) and MRE for LOO-CV were 72.56 and 4.17%, respectively. These findings demonstrate that the established HQSAR model has a good predictive ability and can accurately predict the RI values of plant essential oil constituents. In addition, the molecular contribution maps of the HQSAR model revealed that the RI values of aromatic compounds increase when hydroxyl groups are connected to their alkyl chains. Aliphatic compounds feature long chain alkyl groups, which can lead to an increase in RI values. The above phenomena highlight the promising application prospects of HQSAR for studying the RI values of plant essential oil constituents. Therefore, this study provides a reliable theoretical basis for predicting the RI values of other essential oil constituents.

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The findings suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

Comparing laser-induced breakdown spectroscopy and visible near-infrared spectroscopy for predicting soil properties: A pan-European study

Soil organic carbon (SOC), texture, clay/organic carbon (OC) ratio, and extractable phosphorus are among the key soil descriptors representing the physical, chemical, and biological properties. However, analyzing these soil properties using conventional methods of analysis is time-consuming and often involves the use of hazardous chemicals. Therefore, scaling these methods to analyze more samples covering large geographical distributions within a reasonable turn-around time has become a challenge. In this study, two rapid techniques, namely laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (vis-NIRS), were compared for the prediction of SOC, texture, extractable phosphorus, and clay/OC ratio. A total of 896 samples collected from 23 European countries from the Land Use/Cover Area frame Survey (LUCAS) database were used to represent a wide distribution in terms of soil texture and geographical coverage. The data was manually split into calibration and validation samples while ensuring a representative geographical distribution in the calibration and validation set for the selected samples. Partial least square regression (PLSR) models were developed for both LIBS and vis-NIRS and compared based on prediction accuracy. LIBS outperformed vis-NIRS in predicting SOC, sand content, and clay/OC ratio, with root mean square error of prediction (RMSEP) values of 1.79 %, 12 %, and 7.69, respectively. Conversely, vis-NIRS yielded slightly higher RMSEP values for SOC, sand content, and clay/OC ratio predictions at 1.88 %, 15 %, and 8.13, respectively. Both LIBS and vis-NIRS performed similarly in predicting clay content, with an RMSEP of 6 %. However, there was a low predictive ability for silt and phosphorus concentration, as shown by the weak correlations (R2 < 0.5) between the predicted and reference soil properties. There was no significant difference between LIBS and vis-NIRS predictions for clay, silt, and SOC content (p > 0.05). However, there was a significant difference between the predicted sand content (p = 0.011) and clay/OC ratio (p < 0.001). The overall better performance of LIBS highlights its potential for application in a wide range of soils, especially for soils covering large geographical distributions and varying soil properties. Further, both techniques present opportunities for rapid determination of soil properties important for evaluating soil health on continental scales.

Non-destructive prediction of ready-to-eat kiwifruit firmness based on Fourier transform near-infrared spectroscopy

There is a growing demand for ready-to-eat kiwifruit in the world. However, ready-to-eat kiwifruit has a rather narrow range of firmness (e.g. 10–30 N), and it remains challenging to predict this firmness in a non-destructive manner. Here, we report a strategy for non-destructive prediction of kiwifruit firmness based on Fourier transform near-infrared (FT-NIR) spectroscopy. The radial basis function (RBF) model displayed superior performance, with a coefficient of determination (Rc2) of 0.83, a cross-validation coefficient of determination (Rp2) of 0.73, a root mean square error of calibration (RMSEC) of 0.58, a root mean square error of prediction (RMSEP) of 0.72, and a ratio of performance to deviation (RPD) of 1.92. To enhance the accuracy of kiwifruit firmness prediction, we optimized the FT-NIR algorithm through data preprocessing, feature selection, and dimensionality reduction. The results showed that the FD-CARS-SVR (RBF) algorithm exhibited the best performance in predicting kiwifruit firmness during the shelf life with impressive values of Rc2 (0.99), Rp2 (0.92), RMSEC (0.15), RMSEP (0.40), and RPD (3.48). To further evaluate the applicability of the FT-NIR model, we compared the data predicted by the model and acquired from the Kiwifirm™ and penetrometer GY-4. The results revealed pronounced superiority of the FT-NIR model for the firmness ranging from 10 to 40 N to replace Kiwifirm™, providing a new non-destructive model for the prediction of the firmness of ready-to-eat kiwifruit.

Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging

Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (Rp) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images.

Fourier Transform Infrared Spectroscopy Tracking of Fermentation of Oat and Pea Bases for Yoghurt-Type Products

The fermentation process of plant-based yoghurt (PBY)-like products must be followed for consistency by monitoring, e.g., the pH, temperature, and lactic acid concentration. Spectroscopy provides an efficient multivariate in situ quality monitoring method for tracking the process. Therefore, quality monitoring methods for pea- and oat-based yoghurt-like products using Fourier transform infrared (FT-IR) spectroscopy and high-performance liquid chromatography (HPLC) were developed and modeled. Plant-based yoghurt (PBY) was formulated by fermenting pea and oat plant drinks with a commercial starter culture based on Lactobacillus and Streptococcus strains. The main variance during fermentation was explained by spectral carbohydrate and protein bands with a notable shift in protein band peaks for the amide II band at 1548 cm−1 to 1576 cm−1. In addition to the identification of changed spectral bands during fermentation, FT-IR efficiently tracked the variation in oat and pea fermentation using pH as the main indicator. Prediction models with an R2 for the predicted value of pH as a fermentation indicator (R2 = 0.941) with a corresponding root-mean-squared error of prediction (RMSEP) of 0.247 was obtained when compared to the traditional pH method.

Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.