Hyperspectral imaging for non-destructive prediction of total nitrogen concentration in almond kernels

Hosseini Bai, S ORCiD: 0000-0001-8646-6423© 2018 ISHS. There is increasing awareness of the need to consume high-quality foods because of health concerns. Food safety and health awareness campaigns have provided an impetus for non-destructive and real-time methods for food quality assessment. Total nitrogen is used as an indicator of crude protein content in foods and we examined the potential of hyperspectral imaging to predict total nitrogen concentration in four brands of almonds purchased from commercial retailers. A hyperspectral imaging system in the wavelength range 400-1000 nm was used in the study. A partial linear squares regression (PLSR) model was developed, which predicted total nitrogen concentration with a determination coefficient (R2p) of 0.82 and a root mean error square of calibration (RMSEC) of 0.16. These results indicated that hyperspectral imaging has great potential to predict total nitrogen concentration of almond kernels

Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique

Hyperspectral image analysis in laboratory-based settings has the potential to estimate soil elements. This study aimed to explore the effects of soil particle size on element estimation using visible-near infrared (400–1000 nm) hyperspectral imaging. Images were captured from 116 sieved and ground soil samples. Data acquired from hyperspectral images (HSI) were used to develop partial least square regression (PLSR) models to predict soil available aluminum (Al), boron (B), calcium (Ca), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P) and zinc (Zn). The soil available Al, Fe, K, Mn, Na and P were not predicted with high precision. However, the developed PLSR models predicted B (R2 CV = 0.62 and RMSECV = 0.15), Ca (R2 CV = 0.81 and RMSECV = 260.97), Cu (R2 CV = 0.74 and RMSECV = 0.27), Mg (R2 CV = 0.80 and RMSECV = 43.71) and Zn (R2 CV = 0.76 and RMSECV = 0.97) in sieved soils. The PLSR models using reflectance of ground soil were also developed for B (R2 CV = 0.53 and RMSECV = 0.16), Ca (R2 CV = 0.81 and RMSECV = 260.79), Cu (R2 CV = 0.73 and RMSECV = 0.29), Mg (R2 CV = 0.79 and RMSECV = 45.45) and Zn (R2 CV = 0.76 and RMSECV = 0.97). RMSE of different PLSR models, developed from sieved and ground soils for the corresponding elements did not significantly differ based on the Levene's test. Therefore, this study indicated that it was not necessary to grind soil samples to predict elements using HSI. © 2018 Elsevier B.V

Hyperspectral imaging for non-destructive prediction of total nitrogen concentration in almond kernels

© 2018 ISHS. There is increasing awareness of the need to consume high-quality foods because of health concerns. Food safety and health awareness campaigns have provided an impetus for non-destructive and real-time methods for food quality assessment. Total nitrogen is used as an indicator of crude protein content in foods and we examined the potential of hyperspectral imaging to predict total nitrogen concentration in four brands of almonds purchased from commercial retailers. A hyperspectral imaging system in the wavelength range 400-1000 nm was used in the study. A partial linear squares regression (PLSR) model was developed, which predicted total nitrogen concentration with a determination coefficient (R2p) of 0.82 and a root mean error square of calibration (RMSEC) of 0.16. These results indicated that hyperspectral imaging has great potential to predict total nitrogen concentration of almond kernels

Effects of biochar on soil available inorganic nitrogen: A review and meta-analysis

The interaction between biochar and soil changes nitrogen (N) dynamics in different ecosystems. Although multiple studies have reported influences of biochar on soil inorganic N (SIN) including ammonium (NH4+-N) and nitrate (NO3−-N), the influences reported are contradictory. We undertook a meta-analysis to investigate how biochar properties and the interaction among biochar, soil and fertilisation affect SIN. This quantitative analysis used 56 studies with 1080 experimental cases from manuscripts published between 2010 and 2015. Overall, we found that biochar reduced SIN regardless of experimental conditions (approximately − 11 ± 2% of NH4+-N and − 10 ± 1.6% of NO3−-N); however, 95% of cases were observed within one year after biochar application. SIN was best explained by residence time of biochar in soil, pyrolysis temperature, application rate, fertiliser type, and soil pH. The effects of biochar were complex due to the interaction of biochar with environmental factors. Most biochar trials used wood as a feedstock, but woody biochar did not decrease SIN as much as other plant-derived biochars. When biochar was used with NH4-based fertilisers, SIN decreased compared to biochar with no fertiliser. In contrast, adding organic fertiliser with biochar increased SIN compared to biochar alone. SIN was clearly reduced after one month of biochar application, suggesting that biochar should be applied at least one month prior to planting so plants are not affected by decreased N. Our results revealed that the interactions between biochar and environmental factors, pyrolysis temperature of biochar and biochar surface properties are the main driving factors affecting SIN. There were limited long-term studies of > 1 year, thus the long-term effects of biochar on SIN still remain unclear.Associated Grant:Seed Funding from University of the Sunshine Coast and Griffith University;Associated Grant Code:USC/CRN2012/03 & (EFC-JR

Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions

The common methods of determining soil carbon (C), nitrogen (N) and their isotopic compositions (δ13C and δ15N) are expensive and time-consuming. Therefore, alternative low-cost and rapid methods are sought to address this issue. This study aimed to investigate the potential of hyperspectral image analysis to predict soil total carbon (TC), total nitrogen (TN), δ13C and δ15N. Hyperspectral images were captured from 96 ground soil samples using a laboratory-based visible to near-infrared (VNIR) hyperspectral camera in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) models were developed to correlate the values of TC, TN, δ13C and δ15N, obtained from isotope ratio mass spectrometry method, with their spectral reflectance. The developed models provided acceptable predictions with high coefficient of determination (R2c) and low root mean square error (RMSEc) of calibration set for TC (R2c = 0.82; RMSEc = 1.08%), TN (R2c = 0.87; RMSEc = 0.02%), δ13C (R2c = 0.82; RMSEc = 0.27‰) and δ15N (R2c = 0.90; RMSEc = 0.29‰). The prediction abilities of the models were then evaluated using the spectra of an external test set (24 samples). The models provided excellent predictions with high R2t and ratio of performance to deviation (RPD) of test set for TC (R2t = 0.76; RPD = 2.02), TN (R2t = 0.86; RPD = 2.08), δ13C (R2t = 0.80; RPD = 2.00) and δ15N (R2t = 0.81; RPD = 1.94). The results indicated that the laboratory-based hyperspectral image analysis has the potential to predict soil TC, TN, δ13C and δ15N. © 2018 Elsevier B.V

Using laboratory-based hyperspectral imaging method to determine carbon functional group distributions in decomposing forest litterfall

Studying C functional group distributions in decomposing litterfall samples is one of the common methods of studying litterfall decomposition processes. However, the methods of studying the C functional group distributions, such as 13C NMR spectroscopy, are expensive and time consuming and new rapid and inexpensive technologies should be sought. Therefore, this study examined whether laboratory-based hyperspectral image analysis can be used to predict C functional group distributions in decomposing litterfall samples. Hyperspectral images were captured from ground decomposing litterfall samples in the visible to near infrared (VNIR) spectral range of 400–1000 nm. Partial least-square regression (PLSR) and artificial neural network (ANN) models were used to correlate the VNIR reflectance data measured from the litterfall samples with their C functional group distributions determined using 13C NMR spectroscopy. The results showed that alkyl-C, O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1, aryl-C2 and carboxyl derivatives could be acceptably predicted using the PLSR model, with R2 values of 0.72, 0.73, 0.71, 0.74, 0.76, 0.75 and 0.63 and ratio of prediction to deviation (RPD) values of 1.86, 1.82, 1.78, 1.71, 1.90, 1.76 and 1.43, respectively. Predicted O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1 and aryl-C2 using the ANN model provided R2 values of 0.62, 0.68, 0.69, 0.82 and 0.67 and the RPDs of 1.54, 1.76, 1.52, 2.10 and 1.72, respectively. With the exception of aryl-C1, the PLSR model was more reliable than the ANN model for predicting C functional group distributions given limited amount of training data. Neither the PLSR nor the ANN model could predict the carbohydrate-C and O-aryl-C acceptably. Overall, laboratory-based hyperspectral imaging in combination with the PLSR modelling can be recommended for the analysis of C functional group distribution in the decomposing forest litterfall samples. © 2018 Elsevier B.V

The effects of short term, long term and reapplication of biochar on soil bacteria

Hosseini Bai, S ORCiD: 0000-0001-8646-6423Biochar has been shown to affect soil microbial diversity and abundance. Soil microbes play a key role in soil nutrient cycling, but there is still a dearth of knowledge on the responses of soil microbes to biochar amendments, particularly for longer-term or repeated applications. We sampled soil from a field trial to determine the individual and combined effects of newly applied (1 year ago), re-applied (1 year ago into aged biochar) and aged (9 years ago) biochar amendments on soil bacterial communities, with the aim of identifying the potential underlying mechanisms or consequences of these effects. Soil bacterial diversity and community composition were analysed by sequencing of 16S rRNA using a Miseq platform. This investigation showed that biochar in soil after 1 year significantly increased bacterial diversity and the relative abundance of nitrifiers and bacteria consuming pyrogenic carbon (C). We also found that the reapplication of biochar had no significant effects on soil bacterial communities. Mantel correlation between bacterial diversity and soil chemical properties for four treatments showed that the changes in soil microbial community composition were well explained by soil pH, electrical conductivity (EC), extractable organic C and total extractable nitrogen (N). These results suggested that the effects of biochar amendment on soil bacterial communities were highly time-dependent. Our study highlighted the acclimation of soil bacteria on receiving repeated biochar amendment, leading to similar bacterial diversity and community structure among 9-years old applied biochar, repeated biochar treatments and control. © 2018 Elsevier B.V

The potential of hyperspectral images and partial least square regression for predicting total carbon, total nitrogen and their isotope composition in forest litterfall samples

Purpose: The main objective of this study was to examine the potential of using hyperspectral image analysis for prediction of total carbon (TC), total nitrogen (TN) and their isotope composition (δ13C and δ15N) in forest leaf litterfall samples. Materials and methods: Hyperspectral images were captured from ground litterfall samples of a natural forest in the spectral range of 400–1700 nm. A partial least-square regression model (PLSR) was used to correlate the relative reflectance spectra with TC, TN, δ13C and δ15N in the litterfall samples. The most important wavelengths were selected using β coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set. Results and discussion: The results showed that the data of TC and δ13C could not be fitted to the PLSR model, possibly due to small variations observed in the TC and δ13C data. The model, however, was fitted well to TN and δ15N. The cross-validation R2cv of the models for TN and δ15N were 0.74 and 0.67 with the RMSEcv of 0.53% and 1.07‰, respectively. The external validation R2ex of the prediction was 0.64 and 0.67, and the RMSEex was 0.53% and 1.19 ‰, for TN and δ15N, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and δ15N, showing that the models were reliable for the prediction of TN and δ15N in new forest leaf litterfall samples. Conclusions: The PLSR model was not successful in predicting TC and δ13C in forest leaf litterfall samples using hyperspectral data. The predictions of TN and δ15N values in the external litterfall samples were reliable, and PLSR can be used for future prediction. © 2017, Springer-Verlag GmbH Germany