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[IEEE 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Nagoya, Japan (2019.7.13-2019.7.15)] 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Salt Content Prediction System of Dried Sea Cucumber (Beche-de-mer) Based on Visual Near-Infrared Imaging
摘要： Dried sea cucumber (Beche-de-mer) is a culinary food that is considered luxurious and delicious, especially in China, Korea, and Japan, so the price is quite high. Dried sea cucumber (Beche-de-mer) also has high commercial value and high nutritional value. Their quality determines dried sea cucumber (Beche-de-mer) prices on international markets. One of the parameters that determine its quality is salt content. The excessive salt content in Dried sea cucumber (Beche-de- mer) can cause health problems such as hypertension, stroke, digestive system disorders, etc. Therefore, this paper will discuss a prediction system for measuring salt content in Dried sea cucumber (Beche-de-mer) using hyperspectral imaging technique. This system uses reflectance mode with a wavelength from 400 to1000 nm. The hardware from the prediction system for measuring salt content is motors to generate, hyperspectral camera system, two 150 W halogen lamps, Teflon tables, and personal computer link. Then, the PLSR algorithm is applied to the prediction system model at full wavelength. The prediction model is used to obtain the predicted value of salt content. Then the results of the prediction model are compared with the data references obtained by the mercury nitrate method. The root means square errors and correlation coefficient are used to evaluate the prediction system performance of salt content. The best result of the prediction system in this work is to have a correlation coefficient of 0.99 and root mean square errors of 0.27, respectively, with the number of PLS component is 25. Based on the results of this work, the proposed system can be used as an alternative method of measuring the salt content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability.
关键词： Hyperspectral Imaging,Salt Content,Dried Sea Cucumber,PLSR (Partial Least Square Regression)
更新于2025-09-12 10:27:22
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Direct analysis of calcium in liquid infant formula via laser-induced breakdown spectroscopy (LIBS)
摘要： The present work illustrates the potential of laser-induced breakdown spectroscopy (LIBS) for the direct analysis of liquid food products. The aim of the experiment was to predict calcium content in ready-to-feed infant formula. The analysis was performed by a LIBS system coupled to a liquid sample chamber with a rotatory wheel that presents the liquid to the laser beam as a thin film. Multivariate analysis with partial least squares regression (PLSR) was performed to correlate LIBS spectral data to reference calcium contents. The obtained PLSR model exhibited a good fit and linearity, as indicated by the coefficients of determination for calibration (R2 c) and cross-validation (R2 cv), with values of 0.96 and 0.89, respectively. The robustness of the calibration model was assessed by external validation showing a root-mean-square error of prediction of 6.45 mg 100 mL-1. These results demonstrated the potential of LIBS for real-time analysis of liquid food products.
关键词： Liquid analysis,PLSR,Ready-to-feed infant formula,Calcium,LIBS
更新于2025-09-11 14:15:04
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Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression
摘要： In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.
关键词： alkalinity and salinity,PLSR model,hyperspectral data,soil
更新于2025-09-10 09:29:36
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Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery
摘要： The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity.
关键词： hyperspectral,spectroscopy,West Africa,tropical forest,UAV,Ghana,leaf traits,PLSR,leaf economic spectrum
更新于2025-09-10 09:29:36
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Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging
摘要： This paper described a rapid and non-destructive method based on visible near-infrared (Vis-NIR) hyperspectral imaging system (400–1000 nm) for detection adulteration with duck meat in minced lamb. The multiple average of the reference spectral and a predicted relative spatial distribution coe?cient were applied in this study to reduce the noise of the spectra. The PLSR model with selected wavelengths achieved better results than others with determination of coe?cients (R2 P) of 0.98, and standard error of prediction (RMSEP) of 2.51%. And the prediction map of the duck minced in lamb meat was generated by applying the prediction model. The results of this study indicate the great potential of the hyperspectral technology applying to rapidly and accurately detect the meat adulteration in minced lamb meat.
关键词： Hyperspectral imaging,Minced lamb meat,PLSR,Duck meat,Adulteration
更新于2025-09-10 09:29:36
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Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy
摘要： Iron (Fe) occurs in almost all soils and the analysis of various forms of Fe in the soil is of great pedological interest. Very little is known, however, about how visible and near-infrared (Vis-NIR) spectroscopy performs in intact soil cores of paddy fields for quantifying Fe concentrations. Our objective was to evaluate the feasibility of Vis-NIR spectroscopy of intact soil cores for rapid determination of the four Fe forms: total Fe (Fet), pyrophosphate-extractable Fe (Fep), dithionite-citrate-bicarbonate extractable Fe (Fed), and oxalate-extractable Fe (Feo). A total of 148 intact soil cores in Yujiang County, China, were sampled, and Vis-NIR spectra (350–2500 nm) were sectioned and scanned on four horizontal surfaces (5-cm depth intervals) of each soil core in the laboratory. Partial least squares regression (PLSR) and support vector machine regression (SVMR) models were compared using 70% of the section samples for calibration and 30% for independent validation. Results showed that the nonlinear SVMR models performed better than the PLSR models for the predictions of all Fe forms. The SVMR models produced the best predictions in the independent validation set for Fed (RMSEP = 2.223; R2 P = 0.85; RPDP = 2.59), Fet (RMSEP = 3.693; R2 P = 0.82; RPDP = 2.32), and Fep (RMSEP = 0.086; R2 P = 0.79; RPDP = 2.17). It was concluded that Vis-NIR spectroscopy coupled with SVMR is suitable for quantitatively determining different Fe forms in intact soil cores of paddy fields.
关键词： intact soil cores,iron forms,SVMR,Vis-NIR spectroscopy,paddy fields,PLSR
更新于2025-09-09 09:28:46