Partial Least Squares Regression Prediction Research Articles

SERS-based Ag NCs@PDMS flexible substrate combined with chemometrics for rapid detection of foodborne pathogens on egg surface.

Aninnovative methodis introducedbased on the combination of label-free surface-enhanced Raman scattering with advanced multivariate analysis. This technique allows both quantitative and qualitative assessment of Salmonella typhimurium and Escherichia colion eggshells. Using silver nanocubes embedded in polydimethylsiloxane, we consistently achieved Raman spectra of bacteria. The stability of the Ag NCs@PDMS substrate is confirmed using rhodamine 6G over 30days under standard conditions. Principal component analysis (PCA) effectively distinguishes between S. typhimurium and E. coli spectra. Partial least squares regression (PLS) modelsweredeveloped for quantitative determinationof bacteria on egg surfaces, yielding accurate results with minimal error. The S. typhimurium model achieves Rc2 = 0.9563 and RMSEC = 0.601 in calibration, and Rv2 = 0.9113 and RMSEV = 0.907 in validation. Similarly, the E. coli model achieves Rc2 = 0.9877 and RMSEC = 0.322 in calibration, and Rv2 = 0.9606 and RMSEV = 0.579 in validation. Recoveriesvalidate PLS predictions by inoculating egg surfaces with varying bacterial amounts. Our study demonstrates the feasibility of SERS-PLS for quantitative determinationof S. typhimurium and E. coli on eggshells, promising enhanced food safety protocols.

Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study.

Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy.

Optical Analysis Method for Turbid Media Based on Wedge-Shaped Cells at Different Angles.

The wedge-shaped sample cell, by offering a comprehensive representation of scattering information in turbid media, significantly enhances the informational content conveyed by spectral images compared to flat sample cells. To further refine the accuracy of turbid medium component detection utilizing wedge-shaped sample cells, this work undertakes modeling and analysis of the influence of different wedge angles on detection precision. In this study, employing a 5° gradient in the incident angle of light, we investigate the impact of incident angles ranging from 10 to 45° on the turbid medium component analysis. Validation experiments are performed by utilizing solutions of Indian ink and fat emulsion at varying ratios. Experimental findings demonstrate that under identical experimental conditions, the wedge-shaped sample cell model at an incident angle of 35° yields optimal analysis results. Utilizing partial least-squares regression (PLSR) for the corresponding optical parameters, the highest value of Rp reached 0.980, with an RMSEP of 0.002. When compared to the model with a 30° incident angle, Rp increased by 0.033, and RMSEP decreased by 0.008. In comparison to the flat sample cell model, Rp increased by 0.041, and RMSEP decreased by 0.004. This study, through continuous variation of wedge angles and PLSR modeling and prediction, further enhances the accuracy of turbid medium component detection, laying an experimental foundation for subsequent analysis of turbid medium components based on wedge-shaped sample cells.

基于可见/近红外光谱的鲜枣可溶性固形物在线检测

Soluble solid content (SSC) is one of the important evaluation indexes of the internal quality and taste of fresh jujube. In order to realize the online nondestructive detection of SSC of fresh jujube, this paper took Huping jujube as the research object, adopted self-constructed nondestructive online testing system to collect the spectral information of jujubes (350~2500 nm), and studied the influence of the rotational speed of 4 r/min on the online prediction model of SSC of jujube. Kennard-Stone (KS) algorithm was used to divide the sample into correction set and prediction set. Six commonly used preprocessing methods such as SG smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), orthogonal signal correction (OSC), first derivative (FD), and second derivative (SD) were applied to the spectral data, and the regression coefficient (RC) algorithm and the successive projections algorithm (SPA) were utilized to select informative wavelengths, and a quantitative prediction model for the SSC of Huping jujube was established using partial least squares regression (PLSR). The results indicate that the PLSR prediction model established by preprocessing the original spectrum with OSC and combining it with RC algorithm to select characteristic wavelengths was optimal. Therefore, when predicting the SSC of Huping jujube, the optimal model was OSC-RC-PLSR, and the correlation coefficients of the correction set and prediction set were 0.846 and 0.782, respectively, and the corrected root mean square error (RMSEC) and predicted root mean square error (RMSEP) were 1.962 and 2.247, respectively. The results show that non-destructive detection of soluble solid content of jujube can be achieved by combining visible-near-infrared spectroscopy and appropriate regression model, which provides an innovative way for online sorting and identifying fresh jujube.

A novel non-integer order Savitzky–Golay derivative function of visible and near-infrared spectra for improving prediction accuracy of phosphorus in pig manure

Prediction of phosphorus (P) content in manure using visible and near infrared (vis-NIR) spectroscopy is essential for better manure management. However, accurate prediction requires better spectra pre-processing, out of which spectra derivative is frequently used. This study presents the development of a non-integer order Savitzky-Golay derivative function (NISGDF) of vis-NIR spectra for the analysis of P composition in manure. A formula for a NISGDF is developed and presented in the form of a matrix. Next, both quantitative and qualitative analyses were performed using developed software packages, all of which can be easily called by a command line. Simulated absorption bands using the Gaussian model were operated by NISGDF to assess its abilities in reduction of noise and elimination of baseline and offset. A total of 110 pig manure samples were collected and scanned by a fibre type vis–NIR spectrophotometer in the range of 305–1700 nm. The collected manure spectra were subjected to the NISGDF, before they were used to establish the partial least squares regression (PLSR) model for P. Results indicate that the NISGDF offers significant flexibility in interpolating between the integer order SG derivatives, and reduces baseline offsets and tilts compared to conventional integer order Savitzky-Golay derivative function (ISGDF). The PLSR prediction accuracy with NISGDF was improved by 5–8%, compared with the corresponding PLSR model using the traditional ISGDF. This work shows that the NISGDF offers the advantage of wider applicability and better performance for the prediction of manure P, hence, it is recommended as a more general and better algorithm than the traditional ISGDF for the prediction of P.

Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy.

The potential of four dimension reduction methods for near-infrared spectroscopy was investigated, in terms of predicting the protein, fat, and moisture contents in lamb meat. With visible/near-infrared spectroscopy at 400-1050 nm and 900-1700 nm, respectively, calibration models using partial least squares regression (PLSR) or multiple linear regression (MLR) between spectra and quality parameters were established and compared. The MLR prediction models for all three quality parameters based on the wavelengths selected by stepwise regression achieved the best results in the spectral region of 400-1050 nm. As for the spectral region of 900-1700 nm, the PLSR prediction model based on the raw spectra or high-correlation spectra achieved better results. The results of this study indicate that sampling interval shortening and of peak-to-trough jump features are worthy of further study, due to their great potential in explaining the quality parameters.

Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in Brassica juncea

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in Brassica juncea leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (RV2 = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.

Spray characterization in air-assist atomizers using flow-induced acoustic vibrations and multivariate analysis

This feasibility study investigated a new non-intrusive approach employing acoustic chemometrics. The method includes acoustic/vibration data recording obtained utilizing two clamp-on piezoelectric accelerometers and two electret condensers-type microphones mounted on an arc. Principal Component Analysis (PCA) classification models were based on the acoustic FFT spectra from four sensors. The non-dimensional number (X) values correspond to the different breakup regimes comprising a range of air and liquid (water) flow rates in this air-assisted atomizer (one-analyte system). PCA classification model discerns the clusters belonging to similar non-dimensional number (X) values with the maximum variance in the first principal component (PC1) direction for both sensors combined. This study also assesses the utility of the acoustic chemometrics approach for predicting the flow parameter, such as Sauter mean diameter (SMD) based on the Partial Least Squares-Regression (PLS-R). The PLS-R prediction models work best for the 550 mm location with a low root mean square error of prediction (RMSEP) value of 5.443 and a high Pearson correlation coefficient (R2) value of 0.856 when validated using 50% independent data (test set validation). The comparison between the two sensor types demonstrated superior prediction performance for accelerometers for all the prediction models.

A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp

A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of Δ9-tetrahydrocannabinol (Δ9-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91–0.95) and a low standard error of prediction (SEP = 0.02–0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.

Prediction of tenderness of chicken by using viscoelasticity based on airflow and optical technique.

Tenderness is an index for evaluating meat quality. A prediction model of tenderness was established based on the chicken deformation, which was determined by a viscoelasticity system combined with airflow and optical technique. Different preprocessing methods were used to preprocess the deformation. The interval variables that represent the viscoelasticity of the chicken in deformation, were screen by synergy interval partial least squares algorithm (Si-PLS) and moving window partial least squares algorithm (Mw-PLS). The prediction model was established by principal component regression (PCR) and partial least squares regression (PLSR). The optimum PLSR prediction model was established when Mw-PLS was used to screen the interval variables of Savitzy-Golay (S-G) smoothing data. The correlation coefficient and the root mean square error of the calibration set were 0.965 and 0.874 kg, respectively. The corresponding value of the prediction set was 0.943 and 1.005 kg. This research provides a new method to assess the quality of poultry meat that conducts on airflow and optical techniques.

Non-destructive Detection of Sesame Oil Adulteration by Portable FT-NIR, FT-MIR, and Raman Spectrometers Combined with Chemometrics

Edible oils are often adulterated with fixed oils because of their high quality and price. Sesame oil is prone to adulteration due to its high commodity value and popularity. Therefore, a rapid, simple, and non-invasive method to detect adulteration in sesame oil is necessary for quality control purposes. Handheld and portable FT-NIR, FT-MIR, and Raman spectrometers are easy to operate, non-destructive, rapid, and easy to transport for in-situ assessments as well as being cheaper alternatives to traditional instruments. This study aimed to evaluate three different vibrational spectroscopic techniques in detecting sesame oil adulteration with sunflower and canola oil. Sesame oils were adulterated with fixed oils at different concentrations (0 – 25%) (w/w). Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to authenticate pure sesame oil and Partial Least Squares Regression (PLSR) to predict the levels of the adulterant. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). In all three instruments, SIMCA provided distinct clusters for pure sesame oils and adulterated samples with interclass distance (ICD) over 3. Furthermore, FT-NIR and FT-MIR showed excellent performance in predicting adulterant levels with rval&amp;gt;0.96. Specifically, the FT-MIR unit provided more precise classification and PLSR prediction models over FT-NIR and Raman units. Still, all the units can be used as an alternative method to traditional methods such as GC, GC-MS, etc. These units showed great potential for in-situ surveillance to detect sesame oil adulterations.

Quantitative estimation of rare earth element abundances in compositionally distinct carbonatites: Implications for proximal remote-sensing prospection of critical elements

Rare earth elements (REE) became a strategic raw material in the 21st century and carbonatite-related deposits frequently carry potentially economic levels of these critical metals. To determine concentrations of major and trace elements (including REE) conventional analysis of bulk rock geochemical samples by means of inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) is mostly applied. However, these analytical methods are labor-intensive, costly and time-consuming. In contrast, modern Remote Sensing (RS) – e.g. proximal remote sensing or imaging spectroscopy – has become a novel tool for detecting and quantifying geological materials as they offer an efficient, non-destructive and cost-effective way not only to identify different minerals and/or chemical elements but also model their abundances. In this study, we investigated whether Partial Least Squares Regression (PLSR) models linking the laboratory sample reflectance to mineral/geochemical property could be employed for quantitative predictions of bulk LREE and HREE concentrations. The suite included samples from four different geographic regions in India, East Africa and the USA. Our results showed that for such samples the quantitative approaches have some limitations.First, our models required to exclude samples with high modal abundance of hematite and samples with too low REE contents (LREE < 500 μg/g, HREE < 100 μg/g). Furthermore, it was revealed that the sample mineralogy has a significant impact on the PLSR predictions. To achieve reliable models the sample suite had to be divided into two datasets following their mineralogy and geochemistry. The first dataset comprised rocks with increased amounts of strontium (Sr, DatasetSr), where REE were predominantly bound in Sr-carbonates. On the other hand, REE mainly bound in Ca–REE-carbonates characterized the second dataset (DatasetOther). Following this division, we were able to construct valid prediction models for bulk LREE and HREE concentrations using PLSR. However, the detected spectral assignments associated with the REE presence indicated that the predictions were mainly indirect, based on the present mineral phases rather than direct absorptions related to LREE and HREE. This illustrates the difficulty and limitations for further model generalization and the ability to be further transferred to other lithologically diverse carbonatite sites.We posit that this topic requires future systematic investigations using the extended carbonatite datasets – samples having a wide range of REE abundances – collected from lithologically diverse regions. This would enable further validation and re-calibration of the constructed prediction models.

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy.

The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers’ positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior–inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

A method combining ELM and PLSR (ELM-P) for estimating chlorophyll content in rice with feature bands extracted by an improved ant colony optimization algorithm

Using spectral information to detect chlorophyll content in rice canopy leaves quickly, non-destructively and accurately has a great practical significance for rice growth evaluation, precise fertilization and scientific management. In this paper, Japonica rice in northeast China is taken as the research object, and rice canopy hyperspectral data of key growth stages are obtained through plot experiments. Firstly, the standard normal variate (SNV) and SG smoothing method is used to preprocess the hyperspectral data, and based on the processed spectral data and ant colony optimization algorithm (ACO), an improved adaptive updating ant colony optimization algorithm (AU-ACO) is proposed to select feature bands of chlorophyll content by introducing an adaptive adjustment strategy of volatilization coefficient and optimal pheromone updating strategy in different stages, and compared with the standard ACO algorithm and full-band modeling method. Then, taking the extracted feature band and full-band as input, considering the advantages of linear model and nonlinear model, a hybrid prediction model (ELM-P) combining extreme learning machine (ELM) with partial least squares regression (PLSR) is proposed. In this model, PLSR is used to obtain the preliminary prediction of the chlorophyll content in rice, and the linear trend is obtained, and then the ELM with strong nonlinear approximation ability is used to predict the deviation of PLSR model, the final prediction value is obtained by superposition of the two outputs. In order to verify the superiority of the proposed model, PLSR and ELM prediction models are also established by taking the full-band and the feature bands by different extraction methods as input. The simulation experiment results show that under the same prediction model conditions, the feature bands extracted by proposed AU-ACO algorithm as input can reduce the complexity of the model and improve the prediction performance of the model. The determination coefficient (R2-P) of testing set and the determination coefficient (R2-C) of training set for each model are greater than 0.6785, among them, the ELM-P model with the feature band extracted by AU-ACO algorithm as input has the highest prediction accuracy, R2-C and R2-P are 0.7969 and 0.7918 respectively, RMSE-C and RMSE-P are 1.2969 and 1.1293 mg/L respectively, which can provide valuable reference for detecting and evaluating chlorophyll content in Japonica rice.

Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management

How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two fields was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Reflectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering differences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the effects of variety were considered. Predictions from the reflectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their fields to maximize the net profit from the crop.

Reflectance Spectroscopy for Non-Destructive Measurement and Genetic Analysis of Amounts and Types of Epicuticular Waxes on Onion Leaves.

Epicuticular waxes on the surface of plant leaves are important for the tolerance to abiotic stresses and plant–parasite interactions. In the onion (Allium cepa L.), the variation for the amounts and types of epicuticular waxes is significantly associated with less feeding damage by the insect Thrips tabaci (thrips). Epicuticular wax profiles are measured using used gas chromatography mass spectrometry (GCMS), which is a labor intensive and relatively expensive approach. Biochemical spectroscopy is a non-destructive tool for measurement and analysis of physiological and chemical features of plants. This study used GCMS and full-range biochemical spectroscopy to characterize epicuticular waxes on seven onion accessions with visually glossy (low wax), semi-glossy (intermediate wax), or waxy (copious wax) foliage, as well as a segregating family from the cross of glossy and waxy onions. In agreement with previous studies, GCMS revealed that the three main waxes on the leaves of a wild type waxy onion were the ketone hentriacontanone-16 (H16) and fatty alcohols octacosanol-1 (Oct) and triacontanol-1 (Tri). The glossy cultivar “Odourless Greenleaf” had a unique phenotype with essentially no H16 and Tri and higher amounts of Oct and the fatty alcohol hexacosanol-1 (Hex). Hyperspectral reflectance profiles were measured on leaves of the onion accessions and segregating family, and partial least-squares regression (PLSR) was utilized to generate a spectral coefficient for every wavelength and prediction models for the amounts of the three major wax components. PLSR predictions were robust with independent validation coefficients of determination at 0.72, 0.70, and 0.42 for H16, Oct, and Tri, respectively. The predicted amounts of H16, Oct, and Tri are the result of an additive effect of multiple spectral features of different intensities. The variation of reflectance for H16, Oct, and Tri revealed unique spectral features at 2259 nm, 645 nm, and 730 nm, respectively. Reflectance spectroscopy successfully revealed a major quantitative trait locus (QTL) for amounts of H16, Oct, and Tri in the segregating family, agreeing with previous genetic studies. This study demonstrates that hyperspectral signatures can be used for non-destructive measurement of major waxes on onion leaves as a basis for rapid plant assessment in support of developing thrips-resistant onions.

Fusing environmental variables into soil spectroscopy modeling using a novel two-step regression method

Soil spectroscopy modelling has been extensively studied as the cost-effective proximal sensing method for soil total and organic carbon predictions. Soil carbon properties were highly predictable due to the existence of active carbon molecular bonds within the Visible/Near-Infrared (VNIR) wavelength region. However, prediction results are highly variable for soil properties without active molecular bonds within the VNIR region, such as soil pH, sum of bases (SB), and cation exchange capacity (CEC). This research is intended to enhance soil organic carbon (SOC), nitrogen (N), pH, SB and CEC prediction accuracies by fusing categorical environmental variables (soil sample depth class, soil order, landform, and parent material) with continuous soil spectral data. We introduce a novel two-step regression method (2Step-R) to properly integrate the mixed type variables utilizing Partial Least Squares Regression (PLSR) and Ridge Regression in the modelling. Results from our analysis showed that the novel 2Step-R method was capable to improve the standard PLSR prediction model performances from fair (with ratio of performance to deviation or RPD between 1 and 1.4) to acceptable (RPD between 1.4 to 2), particularly for N, pH, and SB predictions. Slight model performance improvements were achieved for SOC and CEC predictions, although RPD values were within the acceptable range. In conclusion, the 2Step-R method is promising to enhance soil prediction performances and offers flexibility to include different types of ancillary model covariates suited to mix categorical soil-environmental and continuous spectral data.

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350–2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.

Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour

Paprika powder is a spice of culinary importance in many homes but it?s powdered form, has been targeted for fraudulent activities intended at consumer deception. Diverse methods have been used to investigate some of these adulterations but there is no report of paprika adulteration with corn flour, although it remains a suspicion. Technologies such as the near infrared spectroscopy (NIRS) possess non-invasive and rapid advantages that could be explored to monitor this type of adulteration. The study aimed to discriminate and quantify different levels of paprika powder adulterated with corn flour, using NIRS. Two authentic paprika types (DP and SP) were purchased from reputable sources in Hungary and artificially adulterated in the laboratory. Three repeats of each adulteration level (40%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 1%) were prepared and scanned with the Metri NIRS respectively, then, analysed with chemometrics: Linear discriminant analysis (LDA) and partial least squares regression (PLSR). LDA showed 100% recognition and prediction accuracies respectively when DP and SP were analyzed separately to discriminate different concentrations of paprika adulteration. LDA models with NIRS recognize corn flour adulteration with 95.55% and predict it with 95.02% accuracy irrespective of the paprika type used in this experiment. PLSR prediction of 40%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 1% corn flour adulteration yielded an R2CV of 0.98 (high accuracy) and a low RMSECV of 1.71 g/100g (low error). Near infrared as a non-invasive technique exhibited good potentials for paprika powder authentication when corn flour is used as an adulterant.

The use of mid-infrared diffuse reflectance spectroscopy for acid sulfate soil analysis

Good management of sulfide minerals and sulfuric acid in Acid Sulfate Soils (ASS) requires cost-effective rapid analytical data for their characterisation. However, the determination of properties in ASS samples using traditional laboratory techniques is expensive and time consuming. Excessive delays in analysis risks sample changes from oxidation. Mid-infrared (MIR) spectroscopy with multivariate regression offers a quicker and cheaper surrogate. This manuscript reports the prediction of some of the following key soil parameters in ASS characterisation using benchtop (Perkin Elmer) and handheld (ExoScan) diffuse reflectance MIR Fourier transform (DRIFT) spectrometers: Total Organic Carbon (TOC), Titratable Actual Acidity (TAA), Extractable Sulfate Sulfur (ESS), Reduced Inorganic Sulfur (RIS), Retained Acidity (RA), Acid Neutralising Capacity (ANC), and Lime Calculation (LC). Three sets of representative ASS soil profiles, comprising 132 samples from hyposulfidic, hypersulfidic and sulfuric materials, and covering a wide range of environments in South Australia were scanned under laboratory conditions. These were combined with reference laboratory data in partial least squares regression (PLSR) calibration models. The calibrations were validated by leave-one-out cross validation, with a further test set available for validation. Predictions with coefficient of determination (R2) > 0.75, were obtained for TOC (0.95), TAA (0.88), RIS (0.86), LC (0.76) and ANC (0.76), but models for ESS (0.66) and RA (0.41) were less satisfactory. The handheld spectrometer performed similarly to the benchtop spectrometer in terms of PLSR prediction accuracies with the potential for in-field sampling. Results thus confirmed the possibility of using MIR spectroscopy for the rapid and cost-effective characterisation of ASS.