A feasibility quantitative analysis of NIR spectroscopy coupled Si-PLS to predict coco-peat available nitrogen from rapid measurements

Abstract Available nitrogen was an important index to evaluate the supply capacity of nitrogen fertilizer in planting environment. The study of coco-peat available nitrogen content rapid detection technology was of great significance for instructing scientific fertilization. In this study, near infrared (NIR) spectroscopy was used to realize rapid quantitative detection of available nitrogen in coco-peat. Seven different spectral pretreatment methods were adopted to pre-process spectral data with detection band range of 1000–2500 nm (full-band), and synergy interval partial least squares (Si-PLS) was used to screen the optimum combination sub-intervals reflecting coco-peat available nitrogen content from the original full-band spectral data and various pre-treated spectral data. The spectral prediction models of coco-peat available nitrogen based on full-band spectral data and optimal combined sub-intervals spectral data were respectively established, the improvement effects of different pretreatment methods on the accuracy of coco-peat available nitrogen spectral prediction models were analyzed, and the performances of the optimal full-band spectral prediction model and combined sub-intervals prediction model were compared. By analysis and comparison, the first derivative combined with Savitzky-Golay (S-G) smoothing was used to pre-process spectral data, Si-PLS was used to screen the spectral data of 1724–1784 nm, 1852–1922 nm, 1923–1999 nm and 2175–2272 nm, and then the optimal spectral prediction model of available nitrogen content in coco-peat could be established by using the four bands spectral data. For the optimal model, the correlation coefficient and root mean square error of calibration set were 0.994 and 6.998 mg/100 g respectively, the correlation coefficient and root mean square error of prediction set were 0.993 and 7.390 mg/100 g respectively, and the RPD was 8.062. It showed that the combination of NIR spectroscopy and Si-PLS could realize coco-peat available nitrogen quickly and accurately quantitative detection, Si-PLS could effectively reduce the input variables of the established model and simplify the complexity of the model. It also provided a reference for development of a coco-peat available nitrogen content rapid detection device based on characteristic band sub-intervals spectral data.

[1]  Budiman Minasny,et al.  Optimizing wavelength selection by using informative vectors for parsimonious infrared spectra modelling , 2019, Comput. Electron. Agric..

[2]  Caio T. Fongaro,et al.  Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring. , 2017, Journal of environmental management.

[3]  A. Posadas,et al.  Quantifying soil carbon stocks and humification through spectroscopic methods: A scoping assessment in EMBU-Kenya. , 2019, Journal of environmental management.

[4]  Masoud Taghizadeh,et al.  Predicting the moisture content and textural characteristics of roasted pistachio kernels using Vis/NIR reflectance spectroscopy and PLSR analysis , 2018, Journal of Food Measurement and Characterization.

[5]  Zhen Liu,et al.  Development and performance test of an in-situ soil total nitrogen-soil moisture detector based on near-infrared spectroscopy , 2019, Comput. Electron. Agric..

[6]  D. Barbin,et al.  Portable NIR Spectrometer for Prediction of Palm Oil Acidity. , 2019, Journal of food science.

[7]  H. Bartholomeus,et al.  Predicting soil microplastic concentration using vis-NIR spectroscopy. , 2019, The Science of the total environment.

[8]  Sylvie Roussel,et al.  Performance comparison between a miniaturized and a conventional near infrared reflectance (NIR) spectrometer for characterizing soil carbon and nitrogen , 2019, Geoderma.

[9]  Xiuying Tang,et al.  A feasibility quantification study of total volatile basic nitrogen (TVB-N) content in duck meat for freshness evaluation. , 2017, Food chemistry.

[10]  G. Micke,et al.  Simultaneous determination of aspartame, cyclamate, saccharin and acesulfame-K in powder tabletop sweeteners by FT-Raman spectroscopy associated with the multivariate calibration: PLS, iPLS and siPLS models were compared. , 2017, Food research international.

[11]  L. Marin,et al.  Designing chitosan based eco-friendly multifunctional soil conditioner systems with urea controlled release and water retention. , 2019, Carbohydrate polymers.

[12]  Jiachao Zhang,et al.  Population characteristics and influential factors of nitrogen cycling functional genes in heavy metal contaminated soil remediated by biochar and compost. , 2019, The Science of the total environment.

[13]  Muhammad Zareef,et al.  Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms. , 2019, Journal of the science of food and agriculture.

[14]  Xiaomin Chen,et al.  Soil acidity, available phosphorus content, and optimal biochar and nitrogen fertilizer application rates: A five-year field trial in upland red soil, China , 2019, Field Crops Research.

[15]  Yufeng Ge,et al.  Prediction of soil organic and inorganic carbon at different moisture contents with dry ground VNIR: a comparative study of different approaches , 2016 .

[16]  S. Minaei,et al.  Rapid measurement of apple quality parameters using wavelet de-noising transform with Vis/NIR analysis , 2019, Scientia Horticulturae.

[17]  Haiying Yu,et al.  Characteristics of nitrogen loss in sloping farmland with purple soil in southwestern China during maize (Zea mays L.) growth stages , 2019, CATENA.

[18]  W. Dai,et al.  Nitrogen leakage in a rice-duck co-culture system with different fertilizer treatments in China. , 2019, The Science of the total environment.

[19]  Y. Fujihara,et al.  Isotopic evidence for seasonality of microbial internal nitrogen cycles in a temperate forested catchment with heavy snowfall. , 2019, The Science of the total environment.

[20]  Nikolaos L. Tsakiridis,et al.  A genetic algorithm‐based stacking algorithm for predicting soil organic matter from vis–NIR spectral data , 2019, European Journal of Soil Science.

[21]  Haitao Shi,et al.  Evaluation of near-infrared (NIR) and Fourier transform mid-infrared (ATR-FT/MIR) spectroscopy techniques combined with chemometrics for the determination of crude protein and intestinal protein digestibility of wheat. , 2019, Food chemistry.

[22]  A. Mouazen,et al.  Assessment of a soil fertility index using visible and near‐infrared spectroscopy in the rice paddy region of southern China , 2019, European Journal of Soil Science.

[23]  F. Hao,et al.  Assessment of lake eutrophication using a novel multidimensional similarity cloud model. , 2019, Journal of environmental management.

[24]  Santanu Phadikar,et al.  State-of-the-art technologies in precision agriculture: a systematic review. , 2019, Journal of the science of food and agriculture.

[25]  J. Pallone,et al.  Detection and identification of açai pulp adulteration by NIR and MIR as an alternative technique: Control charts and classification models. , 2019, Food research international.

[26]  Å. Rinnan,et al.  Application of near infrared reflectance (NIR) and fluorescence spectroscopy to analysis of microbiological and chemical properties of arctic soil , 2007 .

[27]  Francisco M. Padilla,et al.  Responses of soil properties, crop yield and root growth to improved irrigation and N fertilization, soil tillage and compost addition in a pepper crop , 2017 .

[28]  Douglas Fernandes Barbin,et al.  Identification of fiber added to semolina by near infrared (NIR) spectral techniques. , 2019, Food chemistry.

[29]  Abdul Mounem Mouazen,et al.  On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning , 2019, Soil and Tillage Research.

[30]  Minzan Li,et al.  Soil nitrogen content forecasting based on real-time NIR spectroscopy , 2016, Comput. Electron. Agric..

[31]  Marvin H. Hall,et al.  Carbon and Nitrogen Analysis of Soil Fractions Using Near-Infrared Reflectance Spectroscopy , 1991 .

[32]  Yue Zhang,et al.  Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors , 2019, Comput. Electron. Agric..

[33]  M. Vázquez,et al.  Fraud detection in hen housing system declared on the eggs' label: An accuracy method based on UV-VIS-NIR spectroscopy and chemometrics. , 2019, Food chemistry.

[34]  Miguel Quemada,et al.  Predicting N Status in Maize with Clip Sensors: Choosing Sensor, Leaf Sampling Point, and Timing , 2019, Sensors.

[35]  Mara Cristina Pessôa da Cruz,et al.  Mineral nitrogen fertilization effects on lettuce crop yield and nitrogen leaching , 2019, Scientia Horticulturae.

[36]  Syed Ali Hassan,et al.  Precision Agriculture Techniques and Practices: From Considerations to Applications , 2019, Sensors.

[37]  Quansheng Chen,et al.  Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy. , 2019, Food chemistry.

[38]  Oliver Kohlbacher,et al.  Food monitoring: Screening of the geographical origin of white asparagus using FT-NIR and machine learning , 2019, Food Control.

[39]  G. O. Mayrink,et al.  Determination of chemical soil properties using diffuse reflectance and ion-exchange resins , 2018, Precision Agriculture.

[40]  Jian-min Zhou,et al.  Determination of the contents of magnesium and potassium in rapeseeds using FTIR-PAS combined with least squares support vector machines and uninformative variable elimination , 2014 .

[41]  Furong Huang,et al.  Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[42]  Pengcheng Yan,et al.  Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[43]  W. Zeng,et al.  Impact of raindrop diameter and polyacrylamide application on runoff, soil and nitrogen loss via raindrop splashing , 2019, Geoderma.