Application of NIR in Agriculture

Baeten, Vincent; Dardenne, Pierre

doi:10.1007/978-981-15-8648-4_14

Cited by 23 publications

(18 citation statements)

References 33 publications

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“…PSO-FSGC-Ridge, Lasso-Ridge, GA-Ridge, PSO-SVM-Ridge were c predict N and OM simultaneously. The best subset must have higher R 2 and for calibration and prediction set. PSO-FSGC has obtained excellent re calibration set R 2 c = 0.9923, RMSEC = 0.087 and for prediction set R 2 p = 0.98…”

Section: Comparison Between the Proposed Methods With Lasso Genetic Algorithm (Ga Support Vector Machine (Svm)mentioning

confidence: 99%

“…It is a non-invasive procedure with high penetration of radiation beams, suitable for inline use, and requires no (minimum) sample preparation. It has been widely used for food-quality assessment and in various fields, such as agriculture [ 2 ], medicine [ 3 ], and environmental uses [ 4 ]. The Vis-NIR technique is built on the connection between electromagnetic radiation and the material within the 350–2500 nm wavelength range.…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

Guindo

Kabir

Chen

et al. 2021

Sensors

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Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R2) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R2p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R2p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer.

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Section: Comparison Between the Proposed Methods With Lasso Genetic Algorithm (Ga Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

Guindo

Kabir

Chen

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…NIRS gained widespread use in the food industry in the 1980s due to advancement in chemometric techniques to correlate convoluted absorbance peaks to the chemical composition of the analytes using various mathematical tools like principal component analysis (PCA) and partial least squares (PLS) regression (Scotter, 1990). NIR is a commonly used technology for quality control in grain processing facilities (Gradenecker, 2003) as well as crude protein content in livestock forage and feeds (Vincent and Dardenne, 2021). Early work on analysis of biomass energy feedstocks showed the viability of NIRS with PLS to accurately predict corn stover cell wall composition (glucan, xylan, lignin, acetate, and ash) (Sanderson et al, 1996;Gao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

et al. 2022

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Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R2v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R2v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.

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“…Near-infrared (NIR) spectroscopy is a potent and valued physicochemical technique of analysis [1][2][3]. This technique is primarily recognized in the applications to analytical chemistry [4] as a rapid, cost-efficient, simple, and non-destructive technique with nearly no sample preparation required, which appears to be a very attractive and useful alternative tool for qualitative and quantitative analyses in the agriculture [5,6], food [7,8], chemical [9,10], and pharmaceutical industries [11]. However, it is also met with an increasing importance in bioscience [12,13], medicine [14,15], and various applications related to hyperspectral imaging as well [16].…”

Section: Introductionmentioning

confidence: 99%

Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations

et al. 2021

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Anharmonic quantum chemical calculations were employed to simulate and interpret a near-infrared (NIR) spectrum of caffeine. First and second overtones, as well as binary and ternary combination bands, were obtained, accurately reproducing the lineshape of the experimental spectrum in the region of 10,000–4000 cm−1 (1000–2500 nm). The calculations enabled performing a detailed analysis of NIR spectra of caffeine, including weak bands due to the second overtones and ternary combinations. A highly convoluted nature of NIR spectrum of caffeine was unveiled, with numerous overlapping bands found beneath the observed spectral lineshape. To properly reflect that intrinsic complexity, the band assignments were provided in the form of heat maps presenting the contributions to the NIR spectrum from various kinds of vibrational transitions. These contributions were also quantitatively assessed in terms of the integral intensities. It was found that the combination bands provide the decisively dominant contributions to the NIR spectrum of caffeine. The first overtones gain significant importance between 6500–5500 cm−1, while the second overtones are meaningful in the higher wavenumber regions, particularly in the 10,000–7000 cm−1 region. The obtained detailed band assignments enabled deep interpretation of the absorption regions of caffeine identified in the literature as meaningful for analytical applications of NIR spectroscopy focused on quantitative analysis of caffeine content in drugs and natural products.

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Application of NIR in Agriculture

Cited by 23 publications

References 33 publications

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations

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