Partial Least Squares Estimation of Crop Moisture and Density by Near-Infrared Spectroscopy

Cassanelli, Davide; Lenzini, Nicola; Ferrari, Luca; Rovati, Luigi

doi:10.1109/tim.2021.3054637

Cited by 26 publications

(19 citation statements)

References 23 publications

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“…Recently, in our previous paper [7], we evaluated the feasibility of a NIRS approach to the MC measurement. As we showed, the MC in Alfalfa grass can be evaluated by exploiting absorption peaks of water in the near-infrared spectral region.…”

Section: Introductionmentioning

confidence: 99%

“…As discussed in our previous papers [7,8], a robust statistical analysis can help to recover the information of interest, even with the presence of different densities or leafstem ratios in the sample. Accordingly, a statistical analysis on numerous samples must be considered for the development of a model for the MC and density estimation [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement

et al. 2022

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Near-infrared spectroscopy (NIRS) is widely used in fruit and vegetable quality evaluations, usually after harvesting. In particular, the moisture content is a key parameter for determining product quality; processing phase, e.g., drying process; and economical value. NIRS methods are well-established for laboratory practices where the specimens are properly prepared and measurement conditions are well controlled. On the other hand, it is known that in-field NIRS measurements present several difficulties, as many influencing variables, such as mechanical vibrations, electrical and optical disturbances, and dust or dirt in general, can affect the spectral measurement. In this paper, we propose the design and present the prototype of a NIRS-based measuring system for the rapid determination of the moisture content of bales. The new system uses of a halogen lamp illumination unit to recover water absorption spectral data in the range of 900–1700 nm. The compact stainless steel body makes the instrument portable and easy to transport for rapid in-field MC measurements. The prototype system was characterized and its performance extensively evaluated in a laboratory environment. Finally, a preliminary test was carried out, where the moisture contents of 12 freshly harvested crops samples were measured using the partial least squares (PLSs) regression method. The obtained results show that our prototype system can estimate the alfalfa moisture content information with a coefficient of determination R2 of 0.985 and a root mean square relative error of estimation of 7.1%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement

et al. 2022

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“…Measurements based on optical methods are appealing because they avoid any contact with the sample [ 16 ]. Spectroscopic techniques, such as near- and mid-infrared spectroscopy (NIR and MIR), combined with chemometric modeling offer a potential solution to this textile-classification problem.…”

Section: Introductionmentioning

confidence: 99%

Classification of Textile Samples Using Data Fusion Combining Near- and Mid-Infrared Spectral Information

2022

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There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved.

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“…Numerous methods for the detection of moisture content in transformer oil have been reported, which can be classified into chemical [8,9], electrical [10][11][12], acoustic [13] and optical [14][15][16][17][18][19][20][21][22][23][24][25][26] techniques according to the corresponding working principles. Chemical chromatography and Karl-Fischer titration [8,9] are the most common moisture-in-oil detection methods in laboratory by virtue of their low detection limits and high precision.…”

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confidence: 99%

“…Among them, the near-infrared spectroscopy technique (NIRS) helps to determine the moisture content by combining specialized algorithms like partial least squares (PLS), genetic algorithms (GA), etc. under the characteristic wavelength of the absorption peaks of H2O molecule [14,15]. Nevertheless, the low absorption coefficients, beam divergences, and free-space setup make NIRS not so accurate to detect gas or moisture.…”

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confidence: 99%

S-Taper Fiber Based Moisture Sensing in Power Transformer Oil

Jiang

Liu

et al. 2023

IEEE Trans. Instrum. Meas.

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A moisture sensing technique for real-time monitoring of the moisture content in transformer oil based on an S-taper fiber structure, is proposed and experimentally demonstrated, with the advantages of high sensitivity, excellent repeatability, simple fabrication, compact structure and resistance to ambient temperature variation. By analyzing the physical model of the S-taper fiber, the quantitative relationship between the wavelength change of the transmission dip in the transmission spectrum of the S-taper fiber and the moisture content is established. Then the S-taper fibers with different structural parameters, such as the waist diameter and the axial offset, were fabricated in the lab, and actual measurements in transformer oil samples with different moisture content are carried out. The results show that the transmission dip experiences a red-shifts with decreasing moisture, which could be used to correlate/trace moisture content. It is demonstrated that the S-taper fiber achieves higher detection sensitivity with a decreasing waist diameter or increasing axial offset. For the Staper fiber with a waist diameter of 50 μm and an axial offset of 110 μm, the sensitivity and the lower detection limit reach up to 0.48 nm/ppm and 2.19 ppm, respectively. Therefore, the S-taper fiber sensor could effectively in-situ monitor the moisture content in the transformer oil in real-time, which helps to detect the insulation damp problem in the early stage of the transformer in time and ensure its long-term safe operation.

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Partial Least Squares Estimation of Crop Moisture and Density by Near-Infrared Spectroscopy

Cited by 26 publications

References 23 publications

Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement

Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement

Classification of Textile Samples Using Data Fusion Combining Near- and Mid-Infrared Spectral Information

S-Taper Fiber Based Moisture Sensing in Power Transformer Oil

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