Results Of Partial Least Squares Regression Research Articles

Integrated serum pharmacochemistry and serum pharmacology to investigate the active components and mechanism of Bushen Huoxue Prescription in the treatment of diabetic retinopathy

This study aimed to investigate the active components of Bushen Huoxue Prescriptions (BHP), and further clarify the mechanism by the integration of serum pharmacochemistry and serum pharmacology based on spectrum-effect relationship in vivo. In this paper, the components absorbed into serum were analyzed by ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap-high resolution mass spectrometry (UPLC-Q-Exactive Orbitrap-HRMS). And Müller cells were chosen as target cells to further investigate the mechanism. After cell purification, the well-grown cells were identified by Hematoxylin-eosin staining (HE) staining and immunofluorescence assay, such as glial fibrillary acidic protein (GFAP) and glutamine synthetase (GS). The logarithmic phase cells were divided into normal group, model group and 12 BHP groups. The hyperglycemic and hypoxic model was induced by 50 mmol/L glucose and 1 mmol/L sodium disulfite. Enzyme-linked immunesorbnent assay (ELISA) was used to detect the expressions of five factors closely related to DR, named vascular endothelial growth factor (VEGF), hypoxia-inducible factor1-alpha (HIF-1α), protein kinase C-β (PKC-β), angiopoietin-2 (ANG-2) and transforming growth factor-β (TGF-β). Finally, the spectrum-effect relationship was investigated to screen the active components of BHP by partial least squares regression (PLSR). The results showed that 83 metabolic components, containing 30 prototypes and 53 metabolites were found in BHP serum. 12 characteristic common peaks were chosen to establish spectrum-effect relationship. Significantly, all the 12 BHP serum exhibited stronger inhibition on the expression of VEGF, PKC-β, and ANG-2, and the expression of VEGF, PKC-β, ANG-2 was chosen to establish the spectrum-effect relationship in vivo. The results of PLSR revealed that the content of methylation and sulfuration of caffeic acid, dehydroxylation and sulfation of Danshensu, daidzein, O-demethylangolanolin, cryptotanshinone, tanshinone IIA and protopanaxatriol were inversely correlated with VEGF expression of Müller cells; the areas of dihydrocaffeic acid, methylation and sulfuration of caffeic acid, dehydroxylation and sulfation of Danshensu, daidzein, cryptotanshinone, tanshinone IIA were negative correlation with the expression of PKC-β; while the coefficient of hydroxytyrosol sulfation, R-equol, O-demethylangolanolin, dihydrotanshinone IIA, hydrated cryptotanshinone, protopanaxatriol showed negative correlation with the expression of ANG-2. The above results indicated that cryptotanshinone, tanshinone IIA, daidzein and protopanaxatriol need be focused in our future research. In addition, this research idea provides feasible ways to investigate and determine pharmacodyamic material basis and screen the Q-markers of TCM and its formulas.

Interrelation between factors controlling sediment yield in the largest catchment of Peninsular India

The catchment area of the Godavari in Peninsular India shows complex heterogeneous morphologic, climatic, and geological settings. Although the sub-basins of the Godavari indicate a great spatial variation in sediment yield, the role of such morpho-climatic heterogeneity in the sediment yield variation is poorly understood. This study aims to analyze the role of geomorphology, lithology, precipitation, and land use on spatial sediment yield variation in the Godavari basin. Using digital topography data, with the aid of a Geographical Information System (GIS), we delineated 25 sub-basins based on gauging station locations and showed a wide variation of sediment yield, ranging from 28 to 3404 t km−2 yr−1. Several geomorphic attributes were found to be co-dependent; then, we utilized the partial least-square regression (PLSR) technique to recognize the relationship between controlling factors with sediment yield and established a predictive model. Furthermore, we quantitatively determined the most important first-order controlling factors using variable importance for the projection (VIP) method. The results of PLSR and VIP revealed that among 20 geomorphic variables, plan curvature (VIP = 1.61), profile curvature (1.61), LS factor (VIP = 1.51), topographic ruggedness index (VIP = 1.51), slope (VIP = 1.51), topographic wetness index (VIP = 1.29), and steepness index (VIP = 1.06) exert first-order control in sediment yield variation. Moreover, Mesozoic and Precambrian rocks in agricultural, forested, and bare-ground environments collectively play a crucial role under the influence of differential rainfall patterns in the spatial variation of sediment yield as indicated by VIP values higher than 1. Our optimal PLSR model explained 62% of the total variance in average sediment yield with a moderate predictive ability value (Q2 = 0.46). The insights obtained from the results of this study can be utilized for basin-specific erosion and hydrological modeling in a similar environment. Moreover, this study can be helpful to planners and policymakers for better erosion management and soil–water conservation measures in the Godavari basin.

Correlation of plasma generated long-lived reactive species in aqueous and gas phases with different feeding gases

Reactive species are believed to play an important role in the treatment effects in both direct and indirect plasma applications. To better understand plasma chemistry and reveal the reactive species generated in the aqueous and gas phases, this study investigated the reactive species (i.e. H2O2, and ) that were generated using two types of plasma jets with different feeding gases (i.e. air, CO2, and N2) and treatment time (i.e. 0, 5, 10 and 20 min) in both aqueous and gas phases. Under the same treatment conditions, the determinations of reactive species in ambient air (X-variable) were carried out using optical absorption spectroscopy (OAS), and the detection of reactive species in plasma-activated water (PAW, Y-variable) was using wet chemical-based colourimetric methods. For both measurements, OAS spectral features and the contents of reactive species in PAW varied from different feeding gases and treatment times. The correlations of X and Y were developed using partial least squares (PLS) regression modelling. High collinearity (R2CVs ∼ 0.955–1) was shown between the independent X and Y. Based on the PLS regression results, the selectivity ratio (SR) algorithm was used to select the most important spectral wavelengths that are highly related to the functional groups of these reactive species.

Identification of common buckwheat (Fagopyrum esculentum Moench) adulterated in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.

Rapid simultaneous analysis of anti human immunodeficiency virus drugs in pharmaceutical formulation by smart spectrophotometry based on multivariate calibration and least squares support vector machine methods

In this study, two chemometrics methods, including partial least squares regression (PLS) and least squares support vector machine (LS-SVM) were applied for the simultaneous determination of zidovudine (ZDV) and lamivudine (LMV) in synthetic mixtures and anti-HIV pharmaceutical formulation. These approaches along with the spectrophotometric method were used to solve spectral overlapping problems between mentioned components. The results of PLS showed that the number of components for ZDV and LMV were 10 and 10 with mean square prediction error (MSPE) of 0.4045 and 2.1189, respectively. This method revealed recoveries ranging from 99.48% to 100.40% and 99.55% to 101.25% for ZDV and LMV, respectively. By applying leave-one-out cross-validation (LOO-CV), γ (regularization parameter) and σ2 (width of the function) values were found to be 50, 1500 and 210, 20 with root mean square error (RMSE) of 0.6156 and 0.3163 for ZDV and LMV, respectively. The mean recoveries obtained by the LS-SVM were 100.82% and 98.93% for ZDV and LMV, respectively. A comparison between the suggested methods and high-performance liquid chromatography (HPLC) as a reference technique was implemented, which did not show a significant difference. The results obtained in this research revealed that the chemometrics approaches can be efficient, simple, inexpensive, and precise for routine analysis and quality control of the drug.

Evaluation of Aroma Characteristics of Dried Shrimp (Litopenaeus vannamei) Prepared by Five Different Procedures.

Litopenaeus vannamei is one of the most popular shrimp species in the world and has been reported in studies on its dryness and flavor. However, the aroma characteristics of shrimps dried with different drying methods are compared in a unified way, and there are few reports on the difference in aroma of different shrimps dried. In order to clarify the difference in aroma characteristics of shrimp dried produced by different drying methods. In this study, blanched shrimp (BS) was used as a control to analyze the aroma characteristics of shrimp dried by five different procedures (SD-BFDP) samples, namely vacuum freeze-dried shrimp (VFDS), vacuum dried-shrimp (VDS), heat pump-dried shrimp (HPDS), hot air dried-shrimp (HADS) and microwave vacuum-dried shrimp (MVDS). An electronic nose (E-nose) was used to obtain the aroma fingerprint of SD-BFDP samples. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used for qualitative and quantitative analysis of volatile compounds in SD-BFDP samples. Partial least squares regression (PLSR) was used to analyze potential correlations between sensory attributes and aroma-active compounds (AACs). Partial least squares-discrimination analysis (PLS-DA) was used to screen for signature aroma compounds. The results of the E-nose showed that there were differences in the aroma fingerprints of the SD-BFDP samples, and the E-nose could distinguish the five kinds of SD-BFDP. The qualitative and quantitative results of GC-MS showed that the types and contents of the main volatile components of SD-BFDP samples were different. 15 AACs were screened from SD-BFDP based on odor activity value (OAV). The PLSR results showed good correlations between certain sensory attributes and the majority of AACs. PLS-DA results displayed that aroma attributes of SD-BFDP samples could be distinguished by six signature aroma compounds, including trimethylamine, 2,5-dimethylpyrazine, 2-ethyl-5-methylpyrazine, nonanal, 3-ethyl-2,5-dimethylpyrazine, and octanal. These research results reveal that shrimps dried in different procedures have unique aroma characteristics, which could provide a theoretical basis for the rapid identification of aroma attributes of dried shrimps in the future. From a flavor perspective, MVD is the best drying method.

Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy

To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R2 reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy.

Characterization of Pleurotus citrinopileatus hydrolysates obtained from Actinomucor elegans proteases compared with that by commercial proteases.

Pleurotus citrinopileatus, a nutritious and palatable edible mushroom, can be used as an appropriate material to prepare high-grade flavoring agents. Based on this, the current study aimed to investigate the feasibility of a productive protease system from Actinomucor elegans to prepare P. citrinopileatus hydrolysate (PCH). The Actinomucor elegans crude protease (AECP) was prepared from the solid-state fermentation product of P. citrinopileatus by A. elegans. AECP and four commercial proteases (alcalase, neutrase, papain, and protamex) were applied to acquire five kinds of PCHs. The physical-chemical properties of PCHs as well as its concentrationandcomposition of nonvolatile compounds were comparatively analyzed. Sensory evaluation and electronic tongue analysis were utilized to evaluate sensory characteristics. AECP was found to be the most effective protease, with the highest hydrolysis degree (35.91%) and protein recovery (81.46%). The result of molecular weight distribution indicated that peptides below 500Da were the main fraction of AECP hydrolysates, while AECP hydrolysates showed the highest content of monosodium glutamate-like (20.23 ± 0.16mg/g) and flavor 5'-nucleotide (4.30 ± 0.07mg/g) peptides. In summary, the AECP hydrolysate had superior sensory profiles compared with other hydrolysates. In addition, AECP hydrolysates exhibited favorable kokumi taste in which peptides below 500Da showed the highest correlation with kokumi by the results of partial least-squares regression. These results indicated the feasibility of applying PCHs as flavor additives or seasoning in the food industry. AECP might be used as an alternative enzyme choice because of its low cost and high hydrolysis efficiency. PRACTICAL APPLICATION: Pleurotus citrinopileatus served as a potential raw material for natural seasonings because of its high protein content and appropriate ratio of umami amino acids to total amino acids. Enzymatic hydrolysis was an efficient approach to improve the flavor of P. citrinopileatus, where the choice of enzyme was one of the most critical factors. The research indicated that P. citrinopileatus hydrolysate prepared by A. elegans crude protease (AECP) exhibited an acceptable flavor, which provided theoretical support for the high-value utilization of P. citrinopileatus as food seasoning. AECP might be applied as an alternative enzyme resource because of its low cost and high hydrolysis efficiency.

Study on the Spectrum-Effect Correlation of Anti-Inflammatory Active Extract of Sauropus spatulifolius Beille.

Sauropus spatulifolius Beille (S. spatulifolius) is a commonly used medicine of the Bourau and Yao nationalities. However, the composition of S. spatulifolius is complex, and simple chemical fingerprints cannot accurately evaluate the relationship between its composition and efficacy. In this study, high-performance liquid chromatography (HPLC) method was used to establish the fingerprint of the ethyl acetate extract of S. spatulifolius. Based on the evaluation of the similarity of chromatographic fingerprints of traditional Chinese medicine, combined with cluster analysis and principal component analysis (PCA), the common peaks of fingerprints were evaluated. The anti-inflammatory effect data were extracted through the dimethylbenzene-induced ear-swelling model in mice. The gray relational analysis (GRA) combined with partial least squares regression (PLSR) was used to study the spectrum-effect correlation of S. spatulifolius. As a result, the HPLC fingerprint of the ethyl acetate extract of S. spatulifolius was established, and 18 common peaks were identified. Except for S6, the other similarities are all above 0.915. The reference substance control method was used to identify two absorption peaks, namely, protocatechuic acid and caffeic acid. The cluster analysis results showed that 10 samples from different origins were grouped into four categories, which was consistent with the PCA results. Ethyl acetate extract of 10 batches of S. spatulifolius could significantly inhibit the ear swelling of mice (P < 0.01). Through GRA, the order of the contribution of each chemical component to the anti-inflammatory efficacy was obtained. The results of PLSR showed that the VIP values of peaks 3, 4, and 12 were greater than 1 and were positively correlated with anti-inflammatory activity. In this study, the HPLC fingerprint of the ethyl acetate extract of S. spatulifolius was established. Through the study of the spectrum-effect correlation, the anti-inflammatory active substance of the ethyl acetate extract of S. spatulifolius was obtained. The anti-inflammatory effect of S. spatulifolius was the result of the joint action of multiple ingredients. This research helps to quickly and accurately discover the active ingredient groups of traditional Chinese medicine and provides new ideas and methods for studying the effective substances of traditional Chinese medicine.

Adulteration detection of Sudan Red and metanil yellow in turmeric powder by NIR spectroscopy and chemometrics: The role of preprocessing methods in analysis

Turmeric powder is valued both for its medicinal properties and for its popular culinary use, such as being a component of curry powder. Due to its high demand in international trade, turmeric powder has been subject to economically driven and hazardous chemical adulteration. The objective of this study was to classify and quantify two major adulterants (Sudan Red and Metanil yellow) in turmeric powder using near-infrared (NIR) spectroscopy associated with chemometric models. The absorbance spectra of samples were obtained in frequency regions of 10,000–4200 cm−1. A Soft Independent Modeling of Class Analogy (SIMCA) model was established to determine the authenticity of the turmeric powder. A partial least squares (PLS) regression was used to predict the adulteration level. The different combinations of preprocessing methods such as standard normal variate transformation (SNV), median filter, Savitzky-Golay, wavelet, multiplicative scatter correction (MSC), first derivative (D1) and second derivative (D2) were applied to preprocess the spectral data, in which SNV combined with median filter and D1 performed best. The sample correct classification rate in SIMCA model were found 96.7 and 93.4 in both calibration and validation set respectively. The PLS regression models were evaluated by the coefficient of determination (r2) and root mean square error (RMSE). All PLS regression models established with the combination of different spectral pretreatment except of MSC, Savitzky-Golay, D1 and MSC, Wavelet, D1 showed low values of RMSE and ideal coefficient of determination for both calibration and prediction sets while using relatively few factors. Also, the PLS regression results showed that first derivative led to better calibration models when compared with the second derivative. The obtained statistical parameters for Sudan Red were r2 = 0.90, RMSE = 0.059 in test set and r2 = 0.91 and RMSE = 0.058 for metanil yellow in test set.

Investigation on the Q-markers of Bushen Huoxue Prescriptions for DR treatment based on chemometric methods and spectrum-effect relationship

Ethnopharmacological relevanceDiabetic retinopathy (DR) is a kind of complex complication of late diabetes mellitus with high incidence and risk of blindness. Bushen Huoxue Prescription (BHP), which consists of Rehmanniae radix (RR), Salviae miltiorrhizae radix et rhizoma (SMRR), Ginseng radix et rhizome (GRR) and Puerariae lobatae radix (PLR), has an active effect on the treatment of DR. However, the quality markers (Q-markers) of BHP are not entirely clear. PurposeThis study aimed to screen the Q-markers of BHP for DR treatment based on the establishment of spectrum-effect relationship and verified experiment. Materials and methodsIn this study, 12 BHP samples (S1–S12) for fingerprint analysis and pharmacological evaluation were prepared according to a four-factor and twelve-level uniform design. High performance liquid chromatography-ultraviolet detector-evaporative light scattering detector (HPLC-UV-ELSD) was employed to analyze the fingerprint on the basis of the characteristics of BHP components. The evaluation of sample similarity was carried out by similarity analysis (SA) and hierarchical cluster analysis (HCA). The pharmacological indicators, including expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) in the retina of Sprague Dawley (SD) rats induced by streptozotocin (STZ), were detected by enzyme-linked immunosorbent assay (ELISA). Besides, the spectrum-effect relationship between common peaks of fingerprints and the pharmacological results was investigated by partial least squares regression (PLSR) and canonical correlation analysis (CCA). The results of spectrum-effect relationship were verified by the expression of VEGF and HIF-1α on primary culture retinal Müller cells induced by hyperglycemia and hypoxia. ResultsIn the HPLC-UV-ELSD fingerprint, 23 common peaks in UV and 14 common peaks in ELSD were identified. The pharmacological results indicated that the expression of VEGF and HIF-1α in the retina of SD rats was inhibited by 12 BHP samples to varying degrees compared with the model group. Based on SA and heatmap of HCA, S4 and S8 were clearly distinguished from other samples. The results of PLSR and CCA revealed that the contents of puerarin, daidzin, salvianolic acid B and ginsenoside Rb1 were inversely correlated with the expression of VEGF and HIF-1α. Hence, the four compounds may be the main active components to prevent and treat DR. The results of intervention on primary culture retinal Müller cells showed that puerarin, daidzin, salvianolic acid B, and ginsenoside Rb1 can significantly inhibit the expression of VEGF and HIF-1α. ConclusionsThe spectrum-effect relationship of BHP was successfully established, and the Q-markers of BHP for the prevention and treatment of DR were preliminarily confirmed. It provides a feasible method for the research of quality control.

Release Risk of Phosphorus by Sediments and Its Influencing Factors in Ponds and Ditches of a New Urban District Park

This study examined five ponds and three ditches in the Shufengwan Sports Park in a new urban district of Hefei City, from which surface-layer sediments and overlying water samples were collected during autumn, winter, and summer. The equilibrium phosphate concentrations (EPC0) of the sediments and its response to exogenous carbon or nitrogen were then measured. The resulting EPC0values were used to assess the risk of phosphorus release by the sediments. Finally, major factors influencing phosphorous release were identified using the Partial Least Squares Regression (PLSR) method. The sediments in the urban park exhibited a light-to-moderate level of phosphorous pollution, with the total phosphorus content (TP) ranging from 209.28 to 713.51 mg·kg-1 and biologically available phosphorus accounting for 18.51%-36.21% of the total phosphorus content. Under ambient background, the EPC0 values in pond sediments were 0.012-0.142 mg·L-1, with a mean value of 0.057 mg·L-1, while in ditches the values ranged from 0.036 to 0.156 mg·L-1 with an average value of 0.078 mg·L-1. The addition of exogenous carbon increased the EPC0 values (by approximately 47.5% in pond 3), and thus increased the risk of phosphorus release from sediments, in pond 1, 3, and ditch 1. However, EPC0 values of the other ponds and ditches decreased (in particular, by approximately 58.6% in pond 5), indicating that the risk of phosphorus release decreased. After the addition of exogenous nitrogen, the EPC0 values of almost all ponds and ditches declined to varying degrees (except in ditches 1 and 2 during the summer). In particular, in the EPC0 value of pond 2 declined by approximately 51.6%. The declining values imply that nitrogen was a limiting factor in phosphorus uptake by sediments in ponds and ditches. According to the results of PLSR, nitrogen and phosphorus had different effects on the EPC0 values of sediments in ponds and ditches.

Collaborative analysis on differences in volatile compounds of Harbin red sausages smoked with different types of woodchips based on gas chromatography–mass spectrometry combined with electronic nose

In this study, the volatile profiles of Harbin red sausages smoked with pear, oak, apple and beech woodchips were evaluated using the combination of solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC/MS) and electronic nose (E-nose). Comparisons of the colour, texture profile and sensory attributes were also conducted. The E-nose data and GC/MS data were analysed by principal component analysis (PCA), respectively. The correlation between E-nose sensor responses and volatile compounds was evaluated with partial least squares regression (PLSR). The results showed that the smoking process played an important role in the formation of volatile compounds of Harbin red sausage. The smoking process caused an increase in the intensity of smoky odour, and the sausage smoked with different woodchips showed higher smoky odour compared with the unsmoked sausage (P < 0.05). A total of 87 volatile compounds were identified by GC/MS. According to the odour activity values, the characteristic odour of the Harbin red sausages mainly resulted from 22 volatile compounds. PCA of the volatile compounds and E-nose sensors could effectively differentiate sausages that were unsmoked and separately smoked with different woodchips. Furthermore, the PLSR results (P < 0.05, Q2 = 0.619) indicated that there was a high correlation between the E-nose sensors and volatile compounds.

Analysis of taste characteristics and identification of key chemical components of fifteen Chinese yellow tea samples.

In order to explore the taste characteristics and molecular sensory basis of Chinese yellow tea, in this study, quantitative descriptive analysis (QDA) and partial least squares regression (PLSR) were used to analyze the sensory characteristics and chemical components of 15 yellow tea samples from different regions of China. The results showed that: 11 sensory descriptors and their definitions were obtained by QDA, namely, sweet, umami, bitter, sour, astringent, sweet after taste, mellow, neutral, after-taste, thick and tainted taste. The results of variance indicated that there were significant variation in taste sub-attributes of different samples (p <0.05). Principal component analysis indicated that there was a positive correlation between bitter and astringent, between sweet, umami and sour, and between mellow, thick, after-taste and neutral. All yellow tea samples were divided into four categories according to cluster analysis. The results of PLSR showed that there were 22 chemical components that had an important contribution to the taste characteristics of yellow tea, and the chemical components that had an important influence on each taste component were obtained. The identification of key contribution components of taste characteristics in yellow teas will provide a theoretical basis for further research on the directional adjustment and control of tea taste quality.

Contrasted effects of temperature during defoliation vs. refoliation periods on the infection of rubber powdery mildew (Oidium heveae) in Xishuangbanna, China.

Rubber powdery mildew caused by the foliar fungi Oidium heveae is one of the main diseases affecting rubber plantations (Hevea brasiliensis) worldwide. It is particularly serious in sub-optimal growing areas, such as Xishuangbanna in SW China. To prevent and control this disease, fungicides causing serious environmental problems are widely used. Strong correlations between the infection level and the temperature variables were reported previously, but they were related to monthly data that did not allow unraveling the patterns during the entire sensitive period. We correlated the infection level of powdery mildew of rubber trees recorded over 2003-2011 with antecedent 365days daily temperature variables using partial least squares (PLS) regression. Our PLS regression results showed that the infection level of powdery mildew responded differently to the temperature variables of the defoliation and refoliation periods. Further analysis with Kriging interpolation showed that the infection level increased by 20% and 11%, respectively, per 1°C rise of the daily maximum and mean temperature in the defoliation season, while it decreased by 8% and 10%, respectively, per 1°C rise of the daily maximum and temperature difference in the refoliation season. This pattern was likely linked to the effects of temperature on leaf phenology. It seems highly possible that the infection level of powdery mildew increases, as increasing trends of maximum temperature and mean temperature during the defoliation continue.

Tide modeling using partial least squares regression

This research explores the novel use of the partial least squares regression (PLSR) as an alternative model to the conventional least squares (LS) model for modeling tide levels. The modeling is based on twenty tidal constituents: M2, S2, N2, K1, O1, MO3, MK3, MN4, M4, SN4, MS4, 2MN6, M6, 2MS6, S4, SK3, 2MK5, 2SM6, 3MK7, and M8. The 1st, 2nd, and 3rd PLSR components are selected from 40 PLSR components for the modeling based on the computed variances, Yloadings, and Yscores. The PLSR results are compared with those of the LS. The normality of the model residuals are evaluated by the Jarque–Bera statistical test. The computed probabilities of the normality test for 1st, 2nd, and 3rd PLSR components and LS are p = 0.0611, p = 0.0656, p = 0.916, and p = 0.0517, respectively, which all indicate p > 0.05, and imply that the residuals are normally distributed. The nature of tide criterion is verified by computing the tidal form factor F. The computed tidal form factor for the 1st, 2nd, and 3rd PLSR components and LS are F = 0.1794, F = 0.1696, F = 0.1599, and F = 0.1848 respectively. All the models satisfy the semidiurnal criterion of 0 ≤ F ≤ 0.25 at the 95% confidence level, since the observed tide is characteristically semidiurnal. The computed coefficient of determination for the 1st, 2nd, and 3rd PLSR components and LS are r2 = 0.9134, r2 = 0.9825, r2 = 0.9933, and r2 = 0.7861 respectively. These results prove that the PLSR model outperformed the conventional LS model, and therefore, a viable alternative to the conventional LS model.

Laser-light backscattering imaging approach in monitoring and classifying the quality changes of sweet potatoes under different storage conditions

Abstract Optical imaging techniques have gained wide attention for quality detection of agricultural and food products. In this work, the non-destructive ability of the laser-light backscattering imaging technique (LLBI) for monitoring and classifying the quality changes of sweet potatoes under different storage conditions was investigated. Freshly-harvested sweet potato root samples were stored at 5 °C, 15 °C and 30 °C for a period of 21 d with 120 samples in each storage group. Laser diode emitting light at 658 nm wavelength along with the camera system was employed to capture the backscattered light from the subjected samples. The acquired backscattering images were then pre-processed and segmented, and the intended backscattering parameters (BP) were extracted. Quality parameters (QP) such as moisture content (MC), soluble solids content (SSC), texture and color properties (L*, a*, b*) were measured using the conventional methods as standard reference data. Multivariate analysis in terms of partial least squares regression (PLSR), principal component analysis (PCA) and linear discriminant analysis (LDA) was carried out to correlate and classify the sweet potatoes based on the BP. Results showed that storage had a significant effect both in the BP and QP of sweet potatoes as well as in the interaction between the BP and the treatments (day and temperature) applied. Among all the QP, SSC gave the most promising results (R = 0.56-0.66; RMSE = 0.76–1.10) across all the storage conditions. The analysis also revealed that 15 °C was the most suitable storage condition with the favourable PLSR results (R > 0.50) in all the examined parameters. Moreover, variations on the BP of the samples with respect to the different storage conditions were correctly classified with over 90 % and 80 % accuracies using the PCA and LDA, respectively. Thus, the study indicates that the LLBI technique is feasible and can be a useful tool for a non-destructive quality measurement and classification of sweet potatoes under different storage conditions.

Multivariate quantitative analysis of metal elements in steel using laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS), an element-detection technology with the advantages of no sample preparation and in situ detection of metal samples, is suitable for the quantitative analysis of metal samples. However, severe spectral interference in the detection of metal samples makes the quantitative analysis difficult. Three quantitative analysis methods, including single-variable calibration, partial least squares regression (PLSR), and support vector regression (SVR), are used to conduct the quantitative analysis of four common metal elements (Manganese (Mn), Chromium (Cr), Vanadium (V), and Titanium (Ti)). The PLSR model adds interference spectrum lines to the model for linear modeling, while the SVR model adds interference spectrum lines to the model for nonlinear modeling. The quantitative analysis results of the nonlinear SVR model are the best. The R square (R2) values of Mn, Cr, V, and Ti are 0.993, 0.995, 0.990, and 0.992, respectively. The root-mean-squared errors of the prediction set of Mn, Cr, V, and Ti are 0.044, 0.045, 0.011, and 0.014, respectively. Therefore, the results of PLSR and SVR are better than the calibration curves of the spectral intensity and concentration due to the influence of multivariate factors. SVR has almost no element bias, while PLSR and the single-variable calibration model have different quantitative results due to the different degrees of influence on spectral lines. These results demonstrate that the combined influence of the spectral interference, background noise, and self-absorption can be suppressed by the nonlinear quantitative analysis model in the steel field using LIBS.

Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange

The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit’s internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the ‘Aiyuan 38’ orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange’s total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R2, RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently.

Investigating regionalization techniques for large-scale hydrological modelling

This work investigates regionalization techniques for large-scale model applications in the frame of a pan-European assessment of water resources covering approx. 740,000 km2 in Western Europe. Using the SWAT platform, four variants of the similarity based regionalization approach were compared. The first two involved unsupervised clustering to define hydrological regions before performing hydrological model calibration, whereas the last two involved supervised clustering after performing calibration. Similarity is defined using Partial Least Squares Regression (PLSR) analysis that identifies watershed physiographic characteristics that are most relevant for the selected hydrological response indices.The PLSR results indicate that typically available watershed characteristics such as geomorphology, land-use, climate, and soil properties describe reasonably well the average hydrological conditions but poorly the extreme events. Regionalization variants considering unsupervised clustering and supervised clustering performed similarly well when using all available information. However, results indicate that supervised clustering uses data more efficiently and may be more suitable when data are scarce. It is demonstrated that parsimonious use of available data can be achieved using both regionalization techniques. Finally, model performance consistently becomes acceptable by calibrating watersheds covering only 10% of the model domain, thus, making the calibration task affordable in terms of time and computational resources required.