Visible And Near-infrared Reflectance Research Articles

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Zanthoxylum bungeanum forest is an important economic forest on the Loess Plateau. The rapid assessment of its soil organic carbon (SOC) and total nitrogen (TN) concentrations is critical for the evaluation of soil quality. Soil samples of different forest ages and soil depths were collected for laboratory analyses and spectral measurements, and the visible and near-infrared reflectance (VNIR) spectral data were processed by mathematical transformations, such as first-order derivative (FD) and second-order derivative, multiple scattering correction, and logarithm of the reciprocal. The importance bands of SOC and TN were screened on the basis of competitive adaptive reweighted sampling. In addition, partial least squares regression and support vector machine regression models were constructed, and the applicability of SOC and TN to the models were compared. Results showed that forest age and soil depth remarkably affect SOC and TN concentrations. Among the calibration and validation models, the combination of the SVMR model with Savitzky–Golay smoothing and FD estimated SOC and TN with the highest accuracy, and the validation accuracy of the surface SOC and TN was higher than that of the bottom depths. Meanwhile, the importance bands involved in the modeling were mainly distributed in 400–600 nm and 1000–2400 nm. The combination of the FD transformation and the SVMR model is suitable for determining SOC and TN concentrations in Z. bungeanum forests, and the model has great potential for the quantitative evaluation of soil quality in the Loess Plateau.

A Spectroscopic Study of Mars-analog Materials with Amorphous Sulfate and Chloride Phases: Implications for Detecting Amorphous Materials on the Martian Surface

The Chemistry and Mineralogy X-ray diffraction (XRD) instrument aboard the Curiosity rover consistently identifies amorphous material at Gale Crater, which is compositionally variable, but often includes elevated sulfur and iron, suggesting that amorphous ferric sulfate (AFS) may be present. Understanding how desiccating ferric sulfate brines affect the spectra of Martian material analogs is necessary for interpreting complex/realistic reaction assemblages. Visible and near-infrared reflectance (VNIR), mid-infrared attenuated total reflectance (MIR, FTIR-ATR), and Raman spectra, along with XRD data are presented for basaltic glass, hematite, gypsum, nontronite, and magnesite, each at three grain sizes (<25, 25–63, and 63–180 μm), mixed with ferric sulfate (+/−NaCl), deliquesced, then rapidly desiccated in 11% relative humidity or via vacuum. All desiccated products are partially or completely XRD amorphous; crystalline phases include starting materials and trace precipitates, leaving the bulk of the ferric sulfate in the amorphous fraction. Due to considerable spectral masking, AFS detectability is highly dependent on spectroscopic technique and minerals present. This has strong implications for remote and in situ observations of Martian samples that include an amorphous component. AFS is only identifiable in VNIR spectra for magnesite, nontronite, and gypsum samples; hematite and basaltic glass samples appear similar to pure materials. Sulfate features dominate Raman spectra for nontronite and basaltic glass samples; the analog material dominates Raman spectra of hematite and gypsum samples. MIR data are least affected by masking, but basaltic glass is almost undetectable in MIR spectra of those mixtures. NaCl produces similar FTIR-ATR and Raman features, regardless of analog material.

A hyperspectral plant health monitoring system for space crop production.

Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.

Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy.

Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R2 = 0.86, R2 = 0.84, R2 = 0.82, and R2= 0.73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively.

Assessing a VisNIR penetrometer system for in-situ estimation of soil organic carbon under variable soil moisture conditions

Soils are one of the main terrestrial carbon sinks and contain more carbon than the combined total in the atmosphere and terrestrial vegetation. A major challenge is quantifying how much carbon is stored in the soil and how much it has been sequestered due to changed management practices. In this study, a visible and near-infrared (VisNIR) penetrometer system was developed to measure in-situ soil VisNIR reflectance spectra to a depth of 90 cm into the soil profile. A spectral library of VisNIR soil spectra collected in air-dry and ground states were used to calibrate Cubist regression models for estimating soil organic carbon (SOC) contents. The External Parameter Orthogonalisation (EPO) transformation was applied to the in-situ spectra to remove soil moisture and other in-situ effects from the spectra. This penetrometer system was used to estimate SOC contents at three sites on three different days under variable soil moisture states. The penetrometer EPO performed better compared to the EPO constructed from moist cores. After applying EPO transformation matrix, soil wetness did not have any influence on soil SOC content estimations. The SOC contents were estimated using the in-situ VisNIR system were validated against lab-measured SOC content with mean R 2 , RMSE, and bias of 0.88, 0.32%, and 0.15%, respectively. It is concluded that the VisNIR penetrometer can potentially be used to measure SOC content rapidly and cost-effectively in-situ with a fine depth resolution. • A penetrometer system was used to acquire in-situ soil VisNIR spectra. • A spectral library from agricultural regions of NSW was used for calibration. • External Parameter Orthogonalisation (EPO) reduced confounding soil moisture effect. • External validation was performed to measure soil organic carbon contents.

VNIR estimation of heavy metals concentrations in suburban soil with multi-scale geographically weighted regression

Visible and near-infrared reflectance (VNIR) spectroscopy serves as a promising alternative to detect heavy metal contamination in suburban soils. However, the traditional regression method assumes a global rather than a non-stationary relationship between soil spectra and heavy metals. This study explored the spatially varying relationships between soil chromophores (e.g., SOM and TFe2O3) and heavy metals, and further investigated its implication for heavy metal estimation with VNIR. Specifically, we (i) explore the spatial patterns and covariation of soil chromophore and heavy metals, (ii) verify the feasibility of using multi-scale geographically weighted regression (MGWR) in the VNIR estimation of heavy metals, and (iii) investigate the potential mechanism underlying the models. Specifically, continuum removal and derivation were used to preprocess the reflectance spectra, and then a random frog algorithm was performed to select candidate feature wavelengths, followed by the removal of wavelengths with a correlation higher than 0.80 to avoid collinearity. Standard errors were calculated to further eliminate the variables with a high variance of coefficients. The remaining wavelengths were retained as inputs of the estimation models. The results demonstrated that MGWR outperformed its competitors, resulting in an increase in the coefficient of determination and a decrease in the residual sum of squares. Our study revealed the spatial heterogeneities of the relationships between heavy metals concentrations and soil spectra, and found that the spatial correlation between heavy metals and TFe2O3 (p < 0.01) was the main reason that Cr and Ni can be estimated by using VNIR. The MGWR method not only improves the capability of VNIR in the estimation of heavy metal concentrations, but promotes our understanding of the estimation mechanism.

Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR Spectroscopy.

Tomato-based products are significant components of vegetable consumption. The processing tomato industry is unquestionably in need of a rapid definition method for measuring soluble solids content (SSC) and lycopene content. The objective was to find the best chemometric method for the estimation of SSC and lycopene content from visible and near-infrared (Vis-NIR) absorbance and reflectance data so that they could be determined without the use of chemicals in the process. A total of 326 Vis-NIR absorbance and reflectance spectra and reference measurements were available to calibrate and validate prediction models. The obtained spectra can be manipulated using different preprocessing methods and multivariate data analysis techniques to develop prediction models for these two main quality attributes of tomato fruits. Eight different method combinations were compared in homogenized and intact fruit samples. For SSC prediction, the results showed that the best root mean squared error of cross-validation (RMSECV) originated from raw absorbance (0.58) data and with multiplicative scatter correction (MSC) (0.59) of intact fruit in Vis-NIR, and first derivatives of reflectance (R2 = 0.41) for homogenate in the short-wave infrared (SWIR) region. The best predictive ability for lycopene content of homogenate in the SWIR range (R2 = 0.47; RMSECV = 17.95 mg kg–1) was slightly lower than that of Vis-NIR (R2 = 0.68; 15.07 mg kg–1). This study reports the suitability of two Vis-NIR spectrometers, absorbance/reflectance spectra, preprocessing methods, and partial least square (PLS) regression to predict SSC and lycopene content of intact tomato fruit and its homogenate.

Estimating cadmium-lead concentrations in rice blades through fractional order derivatives of foliar spectra

Heavy metal pollution in farmland harms the environment and poses a potential risk to human health. Visible and near-infrared reflectance (VNIR) spectroscopy is a promising tool for estimating heavy metal concentrations in plants. Integer-order derivatives (including the first and second) are commonly used to pre-process VNIR data and successfully detect certain spectral signals. However, they fail to detect gradual tilts or curvatures and useful target variable information. In this study, a greenhouse experiment covering 16 pre-treatments of Cd–Pb (cadmium-lead) cross-contamination was designed to collect the VNIR data of rice blades during the late booting stage. A fractional order derivative (FOD) algorithm with increments of 0.1 was utilised to pre-process the spectra of the rice blades to explore the model performance in building relationships between Cd and Pb concentrations and leaf spectra. The results indicated that the inversion with pre-processing of integer-order derivatives was not as good as the optimal results with pre-processing of fractional-order derivatives. The R 2 and RMSE of the Cd estimation reached 0.84 and 4.69 at 0.3 rd order pre-processing, while the R 2 and RMSE of Pb were 0.49 and 191.24 at 1.4 th order pre-processing. These optimal results were better than those with pre-processing of 1 st and 2 nd derivatives, resulting in an increase of R 2 and a decrease of RMSE. These results indicated that fractional-order derivatives outperformed integer-order derivatives for pre-processing the rice blades spectra to estimate Cd–Pb concentrations. Our results demonstrated that FOD is an effective spectral processing routine for heavy metal estimation for Cd–Pb cross-contamination. • VNIR spectra of rice blades under Cd–Pb cross-contamination were measured. • Fractional order derivative (FOD) was used to analyze the spectra of rice blades. • FOD can extract more spectral details than first and second derivatives. • PLSR with 0.3 rd (for Cd) and 1.4 th (for Pb) order yielded the best prediction.

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration.

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination () and the root mean square error () of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the and were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

Comparison of soil organic carbon stocks predicted using visible and near infrared reflectance (VNIR) spectra acquired in situ vs. on sieved dried samples: Synthesis of different studies

There is increasing demand for data on soil organic carbon (SOC) stock (SSOC; kgC m−2), but the acquisition of such data, which relies on the determination of volumetric SOC content (SOCv; gC dm−3), is often tedious or complex. Visible and near infrared reflectance spectroscopy (VNIRS) has proven useful for soil characterization, but has rarely been used for direct prediction of SOCv. The objectives of this work were: (i) to compare SOCv predictions using VNIR spectra collected in situ vs. on 2-mm sieved air-dried soil (laboratory conditions), on three sample sets separately (with in situ spectra collected differently for each set); and (ii) to assess SOCv prediction in independent validation using laboratory spectra from all sets.Predictions of SOCv were more accurate using laboratory than in situ spectra for two sets, but not for the third set, where coarse particles content was rather high and variable. Considering the total set of laboratory spectra, predictions in independent validation (leave-one-site-out) yielded accurate SOCv and SSOC predictions (standard errors of prediction were 1.9 gC dm−3 and 0.36 kgC m−2 at 0–30 cm depth, respectively). This result was achieved using local partial least squares regression (PLSR), based on spectral neighbors, which noticeably outperformed global PLSR (which uses all calibration samples equally), as often reported when using large soil spectral libraries for independent validation.Finally, this work demonstrated that SSOC could be quantified accurately using a VNIRS library built on archive soil samples, which offers important perspectives for SSOC accounting.

A new three-band spectral and metal element index for estimating soil arsenic content around the mining area

Owing to the advantages of fast and non-destructive measurement, visible and near-infrared reflectance (VNIR) spectra have been widely used in the study of heavy metal pollution. However, few studies have focused on the estimation of soil heavy metal concentration by the enhanced joint architecture of spectral indices and metal elements enriched in clay minerals. In this work, a new composite index, namely Three-band Spectral and Metal Element Index (TSMEI), is proposed to retrieve arsenic (As) in soil by utilizing the multi-view spectral information. Based on obtained data, including spectra and the concentration of iron (Fe), potassium (K), aluminum (Al), magnesium (Mg) and arsenic (As) of the soil around the open-pit coal mine area, the three-band spectral index (TBSI) for As, K, Fe, Mg and Al were calculated from four types of spectral data, that is, raw reflectance (R), the first-order derivative of the spectrum (FD), spectral continuum removal (CR) and spectral reciprocal logarithmic (RL). Then, the metal element index (MEI) for As concentration were constructed via estimated content of K, Fe, Mg and Al based on their TBSIs. Finally, the optimized TBSIs and MEIs were used to construct TSMEI, and it was combined with random forest to invert the As concentration. The following conclusions are drawn: TBSIs is significantly better than that dual-band spectral index and single-band spectrum for estimating As content, and the correlations between the TBSIs based on the FD and the As concentration perform best (r ≥ 0.7684). In addition, two/three element MEIs show higher correlation coefficients with As concentration compared to individual metal element. Furthermore, the proposed TSMEI allow high-precision estimation of As content, which acquired highest correlation coefficient and lowest RMSE (r = 0.9732, RMSE = 0.0703). The results confirm that the TSMEI is significantly effective in estimating soil As content.

Total Nitrogen Estimation of Paddy Soil Using Visible and Near Infrared Spectroscopy

The current soil nutrient estimation method is laborious, repetitive, time-consuming and costly, making it less efficient for large-scale soil fertility assessment in precision agriculture practice. This paper discussed the feasibility of visible and near-infrared (Vis-NIR) spectroscopy as an alternative method for rapid measurement of total nitrogen in the soil, which is more efficient for a huge paddy field area. For this purpose, Vis-NIR reflectance spectra (350 – 1750 nm) were acquired on 200 soil samples using spectrometers. Partial Least Squares Regression (PLSR) with full (leave-one-out) cross-validation was used to develop the calibration model between the Vis-NIR soil spectra and the total nitrogen obtained by chemical analysis in laboratory. The coefficient of determination (R2val) and residual prediction deviation (RPD) of the developed calibration model for total nitrogen (Ntot) was 0.78 and 1.86, respectively. The predicted total nitrogen map generated based on the Vis-NIR spectroscopy was comparable with the laboratory analysis’s measured map. This result indicates that the Vis-NIR infrared spectroscopy is the potential to be used for total nitrogen estimation in soil.

Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques

Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling.

VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing

An in situ penetrometer system that can measure profile soil properties rapidly, cost-effectively, and at high vertical resolution would benefit the soil science and agriculture communities. A visible and near infrared (VisNIR) integrated multi-sensing penetrometer system was developed to automatically measure in situ soil VisNIR reflectance spectra, penetration resistance, and insertion depth along a soil profile. The system was tested in 11 agricultural fields in Nebraska, Illinois, Iowa, and South Dakota of the U.S. An independent soil VisNIR spectral library was used to build calibration models for soil property prediction. External Parameter Orthogonalization (EPO) was used to correct for the field intactness of in situ VisNIR spectra. The results showed that EPO was effective in correcting for the spectral disparity between in situ and dry-ground VisNIR spectra. The EPO correction showed an improvement of prediction accuracy of soil total carbon (R2 and RMSE improved from 0.29 and 3.06 % to 0.5 and 0.79 %, respectively) and total nitrogen (R2 and RMSE improved from 0.51 and 0.36 % to 0.62 and 0.06 %, respectively). The system also predicted soil bulk density with an RMSE of 0.12 g cm−3 and R2 of 0.80. It is concluded that the VisNIR multi-sensing penetrometer, along with the use of external soil spectral libraries and the spectral correction algorithm EPO, can lead to a rapid, robust and cost-effective system for in situ high resolution vertical soil sensing.

Improved Mapping of Soil Heavy Metals Using a Vis-NIR Spectroscopy Index in an Agricultural Area of Eastern China

Visible and near-infrared reflectance (Vis-NIR) spectroscopy can provide low-cost and high-density data for mapping various soil properties. However, a weak correlation between the spectra and measurements of soil heavy metals makes spectroscopy difficult to use in predicting incipient risk areas. In this study, we introduce a new spectral index (SI) based on Vis-NIR spectra and use it as a covariate in ordinary cokriging (OCK) to improve the mapping of soil heavy metals. The SI was defined from the highest covariance between spectra and heavy metal content in the partial least squares regression (PLSR) model. The proposed mapping approach was compared with an ordinary kriging (OK) predictor that uses only soil heavy metal data and an OCK predictor that uses soil organic matter (SOM) and Fe as covariates. To this end, a total of 100 topsoil (0-20 cm) samples were collected in an agricultural area near Longkou City, and the contents of As, Pb and Zn in the soil were determined. The results showed that OCK with the SI provided better results in terms of unbiasedness and accuracy compared to other comparative methods. Additionally, we explored the SI through simple strategies based on spectral analysis and correlation statistics and found that the SI synthesized most of the soil properties affected by heavy metals and was not limited to Fe and SOM. In summary, the SI method is cost-effective for improving soil heavy metal mapping and can be applied to other areas.

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis.

Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry’s competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350–2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.

Potential of VNIR spectroscopy for prediction of clay mineralogy and magnetic properties, and its paleoclimatic application to two contrasting Quaternary soil deposits

The properties of phyllosilicates, iron oxides (oxyhydroxides), and magnetic minerals in soils can respond to changes in pedogenic weathering intensity and past climate. Numerous paleoclimate reconstructions using soil mineralogy have made use of only one or two of these mineral types, generally in order to limit analytical effort or expense, thereby foregoing comparisons between different mineralogic proxies. Quaternary soil deposits in China afford a unique opportunity for assessing the potential of visible and near-infrared reflectance (VNIR) spectroscopy for analysis of soil minerals, chemical weathering, and paleoclimate reconstruction. Here, we investigated two typical Quaternary soil types exhibiting different weathering degrees, i.e., a loess-paleosol sequence in northern China and a red earth deposit in southern China. Clay and iron mineralogy from the soils was compared with VNIR data using partial least squares regression (PLSR) and continuum-removal-based multiple linear regression (MLRCR) approaches. VNIR spectra show potential for analysis of pedogenic weathering and paleoclimate reconstruction in weakly to highly weathered soils in that spectral behavior is strongly related to both clay-mineral composition and magnetic properties. Both higher concentrations of minerals and their wider variability can improve model predictive ability. Phyllosilicates and non-magnetic Fe-oxides are predicted more accurately for highly weathered red earth deposits, whereas soil magnetic properties are predicted more accurately for loess-paleosols. The predictors identified by the MLRCR method (D2200, D680, and D900) and the ranges constrained by the PLSR method (650–700 and 2150–2250 nm) deserve more attention in future studies of spectral pedology and chemical weathering. We suggest that VNIR spectroscopy has the potential to be an alternative and broadly applicable tool for reconstructing paleoclimate using soil-mineral properties, substituting for a series of common mineralogic analyses. This would significantly reduce the workload and expense of experiments and improve the resolution of paleoclimate studies.

Bi-directional reflectance and polarization measurements of pulse-laser irradiated airless body analog materials

Space weathering (SW) processes including solar wind irradiation and micrometeorite bombardment produce nano-phase iron (npFe) and amorphous layers that redden and darken the visible and near-infrared (VNIR) reflectance spectra of the surface of airless bodies. To understand how SW may affect the directional reflectance and polarization properties of regolith layers, we carried out pulsed laser irradiation experiments to simulate micrometeorite bombardment process on olivine (OL), orthopyroxene (OPX), anorthosite (AN), ilmenite (ILM), and JSC-1A Lunar Regolith Simulant (LRS). All samples have been equalized with a size distribution of (<45 μm) and the unsieved LRS with (<1 mm) was also used. Depending on each sample's difficulty level of weathering, 2 to 8 irradiations with single pulse energy of 50 mJ were used in the irradiations. We measured the spectral reflectance from 0.5 to 25 μm, bi-directional reflectance distribution function (BRDF) and polarization phase curves at 0.633 μm of the samples before and after irradiations. Scanning electron microscopic images show that the irradiated samples become rougher with produced melts and agglutinates. The VNIR reflectance spectra of all irradiated samples show prominent darkening and reddening, except for the unsieved LSR sample which shows spectral blueing. Compared to that of the original samples, the BRDF of irradiated OL and OPX have both enhanced back- and forward-scattering components, while the other 4 samples have enhanced backscattering but nearly unchanged (AN, ILM, JSC-1A (<45 μm)) or decreased (JSC-1A (<1 mm)) forward scattering. In general, the irradiated samples have larger positive polarization values DOPmax as compared to the original samples, consistent with the Umov law prediction. Within measurement uncertainties, the irradiated samples did not show systematic changes in the minimum polarization value DOPmin and the polarization inversion angle αinv. Using OL as an example, numerical radiative transfer computations verified that, the BRDF of olivine grains with npFe distributed uniformly inside the grains or coated with npFe have enhanced back- and forward scattering components as compared with that of the original grains, consistent with measurement results. A 5-parameter Hapke photometric model inversion has shown that all sample grains except OPX have become more backscattering (with smaller asymmetry parameters) upon the strongest irradiation as compared with the original ones. These results indicate that the directional reflectance properties of space weathered regolith grains of airless bodies may be more backscattering than that of fresh materials.

A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil

Visible and near-infrared reflectance (VNIR) spectroscopy is considered to be a potential and efficient means for monitoring soil arsenic (As) contamination. While current studies mainly focus on the evaluation of models' performance when training and verification samples are collected from the same region, whether the model developed at a specific region can be transferred to other regions is still unclear. To answer this question, this study collected a total of 247 samples for training and verification from regions with different geographical conditions, which are Yuanping and Baoding in northern China, Chenzhou and Hengyang in southern China. Afterward, we proposed a transfer component analysis (TCA) based spectroscopic diagnosis model, which aims at adapting a model learned from one region to other regions. This model was compared with the traditional modeling method in terms of the prediction accuracy by four experiments. The results show that: (1) The traditional modeling method trained by specific regional samples has no transfer capability to different regions, since the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) were 0.02 and 0.65 for the first pair of study areas, 0.01 and 1.01 for the second pair of study areas; (2) A transfer model with favorable predictability can be constructed with the aid of TCA spectral transformation and a small amount off-site samples (R2 and RPD were improved to 0.68 and 1.54 for the first pair of study areas, 0.64 and 1.66 for the second pair of study areas). Results suggest that it is promising to develop potential implementations of transferable spectroscopic diagnosis models for estimating soil As concentrations in large area with lower cost.