ABSTRACT
ABSTRACT: Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories.
RESUMO: Dentre os constituintes do solo, especial atenção é voltada aos teores de argila e de matéria orgânica do solo (MOS), pois, entre outros aspectos, são determinantes para retenção de nutrientes e a formação de agregados no solo, os quais afetam diretamente o potencial produtivo das culturas. Os métodos mais comumente utilizados para quantificação destes constituintes apresentam algumas desvantagens, como o uso de reagentes químicos e a geração de resíduos. Uma alternativa a estes métodos é o uso da espectroscopia no infravermelho próximo (near infrared spectroscopy - NIRS). O objetivo deste trabalho é avaliar modelos de quantificação dos teores de argila e de MOS em amostras de solo utilizando dados espectrais por meio da técnica NIRS. Foram utilizadas 400 amostras de solos com amplitude nos teores de MOS e argila para geração de uma curva de calibração. A argila foi determinada pelo método do densímetro e a MOS por meio da solução sulfocrômica. Para calibração, utilizou-se os espectros originais (absorbância) e com pré-tratamento espectral (Savitzky-Golay derivative) das 400 amostras nos seguintes modelos: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) e Gaussian process regression (GPR). A validação da curva foi realizada com o modelo que apresentou melhor desempenho na calibração (SVM) de duas maneiras: com 40 amostras aleatórias oriundas daquelas utilizadas na calibração e com outras 200 novas amostras desconhecidas. O teor de argila das amostras de solo afeta a capacidade preditiva da curva de calibração da estimativa do teor de MOS pelo NIRS. A validação das curvas apresentou pior desempenho (menor R² e maior RMSE) quando feita a partir de amostras desconhecidas, cujo modelo tende a superestimar os teores mais baixos e subestimar os teores mais elevados de argila e MOS com a curva gerada. Apesar do potencial de predição destes atributos via NIRS, outros estudos de calibração ainda são necessários para que esta técnica possa ser utilizada em laboratórios de análises de solos.
ABSTRACT
Objective: To establish a better near infrared quantitative model for quality control of Glycyrrhizae Radix et Rhizoma of components (moisture,total ash,liquiritin and glycyrrhizic acid) in liquorice,in order to realize rapid detection.Method: The contents of moisture,total ash,liquiritin and glycyrrhizic acid were determined in 97 samples based on the methods set forth in Chinese Pharmacopoeia.Meanwhile,the near infrared spectrum was scanned using near infrared spectroscope.R software was used to screen out the spectral pretreatment and build the quantitative models.Result: The optimum spectral pretreatment method for establishing the near infrared quantitative model of moisture and liquiritin was the first order derivative.For moisture,the correlation coefficients of test and validation were 0.930 0 and 0.929 9,and the root mean square errors were 0.243 2 and 0.203 8,respectively.For liquiritin,the correlation coefficients of test and validation were 0.930 3 and 0.907 6,and the root mean square errors were 0.093 9 and 0.128 9,respectively.The optimum spectral pretreatment method for establishing the near infrared quantitative model of total ash was MSC.The correlation coefficients of test and validation were 0.926 5 and 0.917 7,and the root mean square errors were 0.109 6 and 0.103 7,respectively.The optimum spectral pretreatment method for establishing the near infrared quantitative model of glycyrrhizic acid was SNV.The correlation coefficients of test and validation were 0.918 1 and 0.915 7,and the root mean square errors were 0.274 8 and 0.236 0,respectively.Conclusion: In this study,a better near infrared quantitative models for quality control of components of Glycyrrhizae Radix et Rhizoma were established,with a high accuracy,which laid a foundation for rapid detection of the components in Glycyrrhizae Radix et Rhizoma.