Field measurement using portable NIR spectrometer is becoming a popular way to provide in situ, rapid estimation of soil organic matter content. However, in situ NIR spectra is altered by external ...environmental factors, such as soil moisture. The external parameter orthogonalization (EPO) has been recognized as one of the most effective method to minimize effect of soil moisture on spectra. In general, the root mean squared error of cross validation (RMSECV) was mainly used as an evaluation criterion to optimize the parameters involved in EPO algorithm, which ensures that the wet soil spectra corrected by EPO could have a good quantitative performance, but ignores the spectral similarity between the dry soil spectra and the wet soil spectra corrected by EPO. In this study, we present a simple but effective evaluation criterion for optimization of EPO, which we refer to as the mean of spectral correlation coefficient (MSCC). Compared with the commonly used RMSECV criterion, the spectra corrected by EPO (MSCC) are more similar to the corresponding dry soil spectra, and MSCC is more robust to different spectral pretreatment methods. In addition, the quantitative results of MSCC are equivalent to those of RMSECV, and sometimes slightly better than those of RMSECV.
•An evaluation criterion based on spectral similarity.•MSCC is good evaluation criterion for EPO parameter optimization.•Based on MSCC, the corrected spectra not only have a good quantification performance, but also have high spectral similarity.
Measurement of pulpwood traits from a standing tree has considerable advantage when screening large populations for tree selection. It reduces time and also eliminates requirements of transport, ...powdering, and storing the sample. This study describes estimation of Kraft pulp yield (KPY) in Eucalyptus camaldulensis, E. urophylla, Leucaena leucocephala, and Casuarina junghuhniana by portable NIR spectroscopy of standing trees. Calibration models were developed for KPY estimation using portable NIR spectroscopy for the four species, along with a calibration model for syringyl/guaiacyl (S/G) ratio in E. camaldulensis. The calibration models for KPY showed R2 values ranging from 0.93 (E. camaldulensis) to 0.83 (L. leucocephala), and 0.95 for S/G ratio. The developed calibration models for E. camaldulensis and L. leucocephala were compared with laboratory NIR models, and a variation of <±2.0% was found between both methods. The models were validated by both external and cross validation which showed <2.0% RMSEP (root mean square error of prediction) and <2.0% RMECV (root mean square error of cross validation) in external and cross validations, respectively.
The characterization of soil variations crucial for agriculture is challenging due to soil having different mineral composition and particle-size distribution. Traditional methods are costly and ...time-consuming for large-sized areas. Spectroscopic techniques coupled with chemometrics are alternative ways to overcome these drawbacks. Miniaturized near-infrared (NIR) spectrophotometers provide fast, cost-effective spectra acquisition for assessing soil chemistry and distribution despite challenges like overlapped bands and reduced spectral range. This study presents a pattern recognition strategy to address these limitations, enhancing the use of handheld NIR instruments for soil analysis. The study analyzed 176 soil samples from 15 soil groups in the Northeast region of Brazil. First, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and energy-dispersive X-ray fluorescence spectrometry (EDXRF) were employed to characterize the samples, providing complementary vibrational and elemental information, respectively. Common Dimension Analysis (ComDim) identified links between ATR-FTIR and EDXRF data, aiding soil characterization. The Common Components (CCs) from ComDim were used in Partitioning Around Medoids (PAM) clustering, resulting in five distinct classes based on their mineral composition. These classes showed significant differences in clay and sand contents. With the use of ComDim-PAM, samples were labeled for classification via Partial-least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) using NIR spectra (spectral range: 908–1676 nm) from two handheld instruments (Hand 1 and Hand 2). The SVM models outperformed the PLS-DA models, especially by including variable selection for Hand 1, with test accuracy exceeding 90%. These findings highlight the method's advantages for fast and cost-effective assessment, classification, and soil mapping based on their mineral and particle-size distribution.
Geological and archaeological analysis of stone masonries in standing structures helps reveal information about use of natural resources. At the same time, the study of historical materials is useful ...for conservators and cultural heritage management. Geochemical and petrographic analysis of building material types is usually done through destructive analysis on a few selected samples and can be problematic due to the costs of operations and the size of buildings themselves. This paper demonstrates that the combination of hyperspectral imaging portable Near Infrared (NIR) spectroscopy and Energy Dispersive X-ray Fluorescence (ED-XRF) spectroscopy can be useful for analysing types of raw materials used in distinct construction phases of the inner defensive wall in the citadel of Carcassonne (Aude, France). Stratigraphic analysis of the architecture, short-range spectral remote sensing and portable ED-XRF measurements were combined in an interdisciplinary approach to classify sandstone elements. The experimental protocol for in situ non-destructive analysis and classification of the masonry types allows the investigation of the monument in a diachronic perspective, collecting information to delineate raw materials varieties and their use or re-use through time.
The objective of the present contribution is to use a portable near infrared (NIR) spectrometer (in spectral range of 900–1700 nm) as a rapid, facile, and nondestructive technique in combination with ...ensemble methods, for detection of water in bovine milk samples in concentration range 1% to 30% (v/v). On this matter, the pattern of the milk samples (pure and adulterated) was explored by principal component analysis (PCA). Then, random subspace discriminant ensemble (RSDE) was used for classification. The classification figures of merit for the RSDE method was evaluated in terms of sensitivity (Sen), specificity (Spe), accuracy (Acc), error rate (ER), and reliability (Rel). All the values were satisfactory for calibration, cross‐validation, and prediction sets, which showed the reliability and robustness of the developed model. Furthermore, the performance of RSDE method was compared with partial least square‐discriminant analysis (PLS‐DA) and support vector machine (SVM) as the most common classification techniques. In overall, the RSDE classification outperformed the other tested classification methods (PLS‐DA and RBF‐SVM) in terms of accuracy and reliability. Finally, boosted regression tree (BRT) was used for quantifying the level of water adulterant in milk. The performance of the ensemble regression model was evaluated using regression coefficient (R2) and root mean square error (RMSE). The values of R2 and RMSE in prediction set for BRT were 0.95 and 0.58, respectively. The performance of BRT method was also compared with partial least squares regression (PLSR) which again showed outperformance in comparison with this frequent used regression technique.
The aim of this study is to investigate the novel application of a handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of ...adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec
) in the spectral range of 900-1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and
-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew's correlation coefficient value of 0.75 in the external validation set. In the
-NN model, the highest accuracy and Matthew's correlation coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2
-order derivative and 5
nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.
A mild solar drying process on melon slices was carried out. During the whole process, Near InfraRed (NIR) spectra (900–1700 nm) were collected continuously in reflectance mode using a portable ...spectrometer (Viavi Solutions). Samplings were performed for the determination of weight, moisture content (MC), water activity (a
w
) and colour. Partial least square (PLS) regressions showed good prediction ability for MC, a
w
and colour parameters: MC:
R
2
CV
= 0.99; RMSECV = 2.49%; a
w
:
R
2
CV
= 0.97; RMSECV = 0.03;
a
*:
R
2
CV
= 0.91; RMSECV = 1.13;
b
*:
R
2
CV
= 0.86; RMSECV = 2.49;
C
*:
R
2
CV
= 0.87; RMSECV = 2.52; total colour difference:
R
2
CV
= 0.85; RMSECV = 1.77; Browning Index:
R
2
CV
= 0.86; RMSECV = 11.00. Ratio Performance in Deviation values higher than 3 for MC and a
w
models allowed their use for the estimation of such parameters’ pattern along the process, providing satisfactory predictions. These preliminary data suggested the potential applicability of NIR spectroscopy in monitoring the dehydration process.
Non-destructive, simple and fast techniques for identifying authentic palm oil and those adulterated with Sudan dyes using portable NIR spectroscopy would be very beneficial to West Africa countries ...and the world at large. In this study, a portable NIR spectroscopy coupled with multivariate models were developed for detecting palm oil adulteration. A total of 520 samples of palm oil were used comprising; 40 authentic samples together with 480 adulterated samples containing Sudan dyes (I, II, III, IV of 120 samples each). Multiplicative scatter correction (MSC) preprocessing technique plus Principal component analysis (PCA) was used to extract relevant spectral information which gave visible cluster trends for authentic samples and adulterated ones. The performance of Linear discriminant analysis (LDA) and Support vector machine (SVM) were compared, and SVM showed superiority over LDA. The optimised results by cross-validation revealed that MSC-PCA + SVM gave an identification rate above 95% for both calibration and prediction sets. The overall results show that portable NIR spectroscopy together with MSC-PCA + SVM model could be used successfully to identify authentic palm oils from adulterated ones. This would be useful for quality control officers and consumers to manage and control Sudan dyes adulteration in red palm oil.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Introduction
Process analytical technology (PAT) guidance is implemented in the quality assurance of phytocompounds to achieve the Industry 4.0 concept. Near‐infrared (NIR) and Raman spectroscopies ...are feasible for rapid, reliable quantitative analysis through transparent packaging without removing the samples from their original containers. These instruments can serve PAT guidance.
Objective
This study aimed to develop online portable NIR and Raman spectroscopic methods for quantifying total curcuminoids in turmeric samples through a plastic bag. The method mimicked an in‐line measurement mode in PAT compared with placing samples into a glass vessel (at‐line mode).
Materials and methods
Sixty‐three curcuminoid standard‐spiked samples were prepared. Then, 15 samples were randomly selected as fixed validation samples, and 40 of the 48 remaining samples were chosen as calibration set. The results obtained from the partial least square regression (PLSR) models constructed by using the spectra acquired from NIR and Raman were compared with the reference values from high‐performance liquid chromatography (HPLC).
Results
The optimum PLSR model of at‐line Raman was achieved with three latent variables and a root mean square error of prediction (RMSEP) of 0.46. Meanwhile, the PLSR model of at‐line NIR with one latent variable offered an RMSEP of 0.43. For the in‐line mode, PLSR models created from Raman and NIR spectra had one latent variable with RMSEP of 0.49 and 0.42, respectively. The R2 values for prediction were 0.88–0.92.
Conclusion
The models established from the spectra from portable NIR and Raman spectroscopic devices with the appropriate spectral pretreatments allowed the determination of total curcuminoid contents through plastic bag.
This study aimed to develop online portable NIR and Raman spectroscopic methods, the fesible instruments for Process Analytical Technology (PAT), for quantifying total curcuminoids in turmeric samples through a plastic bag, which is a situation of in‐line measurment mode in PAT, compared to placing samples into a glass vessel (at‐line measurement mode). In summary, PLSR models establised from the portable NIR and Raman devices with appropriate spectral pretreatments allowed the determination of the total curcuminoids contents through plastic bag.
