Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.

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Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Foods ◽

10.3390/foods9020154 ◽

2020 ◽

Vol 9 (2) ◽

pp. 154 ◽

Cited By ~ 13

Author(s):

Hongzhe Jiang ◽

Fengna Cheng ◽

Minghong Shi

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Limit Of Detection ◽

Principal Component ◽

Rapid Identification ◽

Performance Comparison ◽

Correlation Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

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Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Foods ◽

10.3390/foods9040500 ◽

2020 ◽

Vol 9 (4) ◽

pp. 500

Author(s):

Huihui Wang ◽

Kunlun Wang ◽

Xinyu Zhu ◽

Peng Zhang ◽

Jixin Yang ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Hyperspectral Data ◽

Correlation Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

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Storage and transformation of organic matter fractions in cryoturbated permafrost soils across the Siberian Arctic

Biogeosciences Discussions ◽

10.5194/bgd-12-2697-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 2697-2743 ◽

Cited By ~ 11

Author(s):

N. Gentsch ◽

R. Mikutta ◽

R. J. E. Alves ◽

J. Barta ◽

P. Čapek ◽

...

Keyword(s):

Organic Matter ◽

Active Layer ◽

Soil Depth ◽

Stable Carbon Isotope ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Small Scale ◽

Siberian Arctic ◽

Permafrost Soils

Abstract. In permafrost soils, the temperature regime and the resulting cryogenic processes are decisive for the storage of organic carbon (OC) and its small-scale spatial variability. For cryoturbated soils there is a lack in the assessment of pedon-scale heterogeneity in OC stocks and the transformation of functionally different organic matter (OM) fractions such as particulate and mineral-associated OM. Therefore, pedons of 28 Turbels across the Siberian Arctic were sampled in five meter wide soil trenches in order to calculate OC and total nitrogen (TN) stocks within the active layer and the upper permafrost based on digital profile mapping. Density fractionation of soil samples was performed to distinguish particulate OM (light fraction, LF, <1.6 g cm−3), mineral associated OM (heavy fraction, HF, >1.6 g cm−3), and a mobilizable dissolved pool (mobilizable fraction, MoF). Mineral-organic associations were characterized by selective extraction of pedogenic Fe and Al oxides and the clay composition was analyzed by X-ray diffraction. Organic matter transformation in bulk soil and density fractions was assessed by the stable carbon isotope ratio (δ13C) and element contents (C and N). Across all investigated soil profiles, total OC stocks were calculated to 20.2 ± 8.0 kg m−2 (mean ± SD) to 100 cm soil depth. Of this average, 54% of the OC was located in active layer horizons (annual summer thawing layer) showing evidence of cryoturbation, and another 35% was present in the permafrost. The HF-OC dominated the overall OC stocks (55%) followed by LF-OC (19% in mineral and 13% in organic horizons). During fractionation about 13% of the OC was released as MoF, which likely represents the most bioavailable OM pool. Cryogenic activity combined with an impaired biodegradation in topsoil horizons (O and A horizons) were the principle mechanisms to sequester large OC stocks in the subsoil (16.4 ± 8.1 kg m−2; all mineral B, C, and permafrost horizons). About 22% of the subsoil OC stock can be attributed to LF material subducted by cryoturbation, whereas migration of soluble OM along freezing gradients appeared as principle source for the dominating HF (63%) in the subsoil. The large proportion of MoF (15%) in the subsoil suggests a pool of weaker mineral-organic associations as result of the low acidity and presence of basic cations, reductive dissolution of Fe(III) oxides, and the frequent freezing-thawing cycles. Despite the unfavourable abiotic conditions, substantial microbial OM transformation in the subsoil was indicated by low C/N ratios and high δ13C values but this was not reflected in altered LF and HF pool sizes. Partial least square regression analyses suggest that OC accumulates in the HF fraction due to coprecipitation with multivalent cations (Al, Fe) and association with poorly crystalline Fe oxides and clay minerals. Our data show that across all permafrost pedons, mineral-associated OM represents the most important OM fraction but the reactivity of this pool under changing future environmental conditions warrants further attention.

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Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

Forests ◽

10.3390/f12121809 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1809

Author(s):

Chunxu Li ◽

Jinghan Zhao ◽

Yaoxiang Li ◽

Yongbin Meng ◽

Zheyu Zhang

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Near Infrared ◽

Scatter Correction ◽

Organic Matter Content ◽

Model Performance ◽

Matter Content ◽

Partial Least Square ◽

Least Square ◽

Successive Projections Algorithm

In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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Suggestions on the Contribution of Methyl Eugenol and Eugenol to Bay Laurel (Laurus nobilis L.) Essential Oil Preservative Activity through Radical Scavenging

Molecules ◽

10.3390/molecules26082342 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2342

Author(s):

Nikolaos Nenadis ◽

Maria Papapostolou ◽

Maria Z. Tsimidou

Keyword(s):

Essential Oil ◽

Radical Scavenging Activity ◽

Pearson Correlation ◽

Radical Scavenging ◽

Methyl Eugenol ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Positive Contribution ◽

Scavenging Activity

The present study examined the radical scavenging potential of the two benzene derivatives found in the bay laurel essential oil (EO), namely methyl eugenol (MEug) and eugenol (Eug), theoretically and experimentally to make suggestions on their contribution to the EO preservative activity through such a mechanism. Calculation of appropriate molecular indices widely used to characterize chain-breaking antioxidants was carried out in the gas and liquid phases (n-hexane, n-octanol, methanol, water). Experimental evidence was based on the DPPH• scavenging assay applied to pure compounds and a set of bay laurel EOs chemically characterized with GC-MS/FID. Theoretical calculations suggested that the preservative properties of both compounds could be exerted through a radical scavenging mechanism via hydrogen atom donation. Eug was predicted to be of superior efficiency in line with experimental findings. Pearson correlation and partial least square regression analyses of the EO antioxidant activity values vs. % composition of individual volatiles indicated the positive contribution of both compounds to the radical scavenging activity of bay laurel EOs. Eug, despite its low content in bay laurel EOs, was found to influence the most the radical scavenging activity of the latter.

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