Currently, one of the main demands of consumers - especially in large fruits such as watermelon - is for supermarkets to use techniques of non-invasive analysis to enable them to measure the ...sweetness of the fruits at the time of purchase, and thus avoid having to base the choice exclusively on external appearance. In addition, increasing interest is being shown by consumers in knowing the nutritional quality of healthy foods, such as watermelon. Near infrared spectroscopy (NIRS) was used to assess in situ the physicochemical and nutritional quality of half-watermelons, which is the format in which they are usually sold in supermarkets at the beginning of the season, due to their high price. A handheld, new-generation spectrophotometer was used for this purpose, and two modes of analysis, static and dynamic, were studied. The results obtained show the viability of using NIRS technology in dynamic mode at the supermarket level to obtain a reliable measurement of the sweetness of the half-fruits, thus meeting the consumers’ demand for sweet-tasting fruits. Promising results were also obtained for measuring the antioxidant activity of the half-watermelons, thus paving the way for the nutritional labelling of this healthy food at the supermarket level.
•NIRS technology to ensure consumers loyalty in future watermelon purchases.•Watermelons can label with nutritional information using NIRS technology.•NIRS inform consumers in situ of the degree of sweetness of the half-watermelons.•A portable new generation NIRS sensor was evaluated for in situ analysis.
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.
The potential of a portable Near Infrared spectrophotometer compared with that of NIR benchtop equipment is assessed to determine the13C/12C relationship of stable isotopes and the fatty acid ...content. 105 samples of subcutaneous fat of Iberian pigs collected at the time of their slaughter have been analyzed. The analysis of stable isotopes and gas chromatography were the methods of reference used. The samples were analyzed without prior handling (portable and benchtop NIR) and after extracting the fat (benchtop NIR). The results show that with the portable equipment it is possible to determine δ13C (‰), 12 fatty acids, and 5 summations of fatty acids (SFA, MUFA, PUFA, w3, and w6), while with the benchtop NIR equipment it is possible to measure δ13C (‰), 16 fatty acids, and the 5 summationsof fatty acids. The correlation coefficients of the portable equipment were slightly lower than those of the NIR benchtop equipment.
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•Fatty acids and stable isotopes were analyzed in subcutaneous fat of Iberian pigs.•Portable and benchtop NIRS instruments performance were compared.•Portable NIRS instrument allows the quantification of δ13C (‰) and 12 fatty acids.•Benchtop NIRS instrument allows the quantification of δ13C (‰) and 16 fatty acids.•Total MUFA, PUFA, SFA, w3 and w6 were quantified with both instruments.
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to ...develop handheld devices devoted to be easily applied for
in situ
determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or
in situ
analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
Human milk (HM) is vital for newborns and its importance allied to growing donations has contributed to the expansion of HM banks. However, compositional analysis in HM banks faces many challenges ...due to traditional methodologies, making it unfeasible for routine inspection. Therefore, this research aims to develop predictive models for the direct determination of the proximal composition in HM samples. The models were developed using spectra acquired with a portable near-infrared spectrometer (MicroNIR) coupled with partial least squares regression and included samples in different lactation phases (colostrum, transition, and mature) and forms (raw and pasteurized). A total of 408 samples were analyzed to give reliability to the models. The performance of the models was estimated by a complete multivariate analytical validation, which indicated satisfactory results with accuracy (represented by the adjust with correlation coefficients ranging from 0.64 to 0.90, and close results for calibration/prediction errors for each parameter). Using the proposed method, moisture, ash, protein, lipids, carbohydrates, and energetic value were successfully predicted. The method is simple, fast, and robust and can be used routinely in compositional analysis of HM banks as an alternative to the traditional methodologies, offering an immediate response with a single and a quick measurement.
•A fast alternative for compositional analysis in human milk banks was developed.•Moisture, ash, protein, lipids, carbohydrates, and energetic value were determined.•Human milk composition was analyzed by portable near infrared spectrometer.•Portable spectroscopy and multivariate calibration were successfully combined.•Validated models predicted both raw and pasteurized human milk in the same model.
Abstract Sesame oil is one of the most commonly used oils in life. It contains a special antioxidant substance called sesame lignans, which has high nutritional value and pharmacological activity. ...Chromatographic methods are accurate and reliable but not suitable for quality control due to time-consuming. Near-infrared (NIR) quantitative models for quickly determining sesamin, sesamolin and sesame lignans in sesame oil were built using a portable NIR spectrometer combined partial least squares(PLS) algorithm, and the optimal PLS models were developed by comparing the performance of the models with different spectral pretreatment methods and bands selection, the correlation coefficients (R2) were RC2 = 0.98, 0.99, 0.99, RP2 = 0.99, 0.97, 0.94, and the root mean square error (RMSEP) was 2.69 μg/mL, 3.73 μg/mL, and 7.96 μg/mL, respectively. The acceptable results demonstrated that portable NIR spectrometer could be used for monitoring the contents of sesamin, sesamolin, and sesame lignans in sesame oil during the production process to carry out quality control.
•Developed portable NIR spectroscopy was used for quantification of chemical compositions.•Prediction models was developed with improved accuracy for the prediction of chemical parameters.•Portable ...NIR system coupled with Si-GA-PLS delivered optimal results.
In the present research work, portable near-infrared (NIR) spectroscopy coupled with different types of chemometric algorithms like partial least-squares (PLS) regression and some effective variable selection algorithms, i.e., synergy interval-PLS (Si-PLS), genetic algorithm-PLS (GA-PLS) and synergy interval genetic algorithm-PLS (Si-GA-PLS) were used for the quantification of chemical compositions of peanut seed samples; also the Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) models were applied for discrimination of peanut of different regions. The compositional parameters, i.e., total phenolic content (TPC), fat, protein, fiber, carbohydrate, moisture, ash and pH, were estimated. The results of the developed model estimated by applying correlation coefficients of the calibration (Rc) and prediction (Rp); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, GA-PLS and Si-GA-PLS correlated with the classical PLS model. The results of Rp determined for prediction and Rc calibration set differ from 0.7473 to 0.9420 and 0.7794 to 0.9623 correspondingly. These results showed that portable NIR spectroscopy coupled with different chemometric algorithms having the potential to be applied for the prediction of the chemical compositions of peanut seed samples.
The aim of this study was to evaluate the ability of a miniaturized near-infrared spectrometer to predict chemical parameters, technological and quality traits, fatty acids and minerals in intact ...Longissimus thoracis and Trapezius obtained from the ribs of 40 Charolais cattle. Modified partial least squares regression analysis to correlate spectra information to reference values, and several scatter correction and mathematical treatments have been tested. Leave-one-out cross-validation results showed that the handheld instrument could be used to obtain a good prediction of moisture and an approximate quantitative prediction of fat or protein contents, a*, b*, shear force and purge loss with coefficients of determination above 0.66. Moreover, prediction models were satisfactory for proportions of MUFA, PUFA, oleic and palmitic acids, for Fe and Cu contents. Overall, results exhibited the usefulness of the on-line miniaturized tool to predict some beef quality traits and the possibility to use it with commercial cuts without sampling, carcass deterioration nor grinding and consequent meat products' loss.
•Miniaturized handheld NIR spectrometer allows satisfactory prediction of MUFA, PUFA, Cu and Fe in intact beef muscles.•Technological and quality traits of beef can be predicted using a handheld NIR device.•Calibration models' accuracy do not allow on-line prediction of major minerals in beef muscles.
Portable near infrared (NIR) spectrometers are now readily available on the market and with their smaller size, weight and cost have provided the opportunity to analyze forages both on farms and ...directly in the field. As new technologies and new portable NIR instruments become available on the market, calibrations for these instruments become a major constraint due to the costs and time necessary to collect reference data. This study evaluated techniques to transfer calibrations for alfalfa and grass forage samples that were developed for a scanning benchtop monochromator (FOSS 6500, 400–2498 nm, LAB) to a diode array instrument (AuroraNir, 950–1650 nm, DA), a digital light processing instrument (NIR-S-G1, 950–1650 nm, DLP) and a short wavelength instrument (SCiO, 740–1070 nm, SCIO). Alfalfa (N = 612) and grass (N = 516) samples from eight agronomic studies were analyzed by wet chemistry for crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), in-vitro digestibility (IVTD) and NDF digestibility (NDFD) and divided into calibration, test-set, standardization and inoculation/prediction datasets. Different calibration transfer strategies were evaluated: Spectral Bias Correction (SBC), Shenk and Westerhaus algorithm (SW), Piecewise Direct Standardization (PDS), Dynamic Orthogonal Projection (DOP) or creating a new calibration using LAB predictions of the inoculation/prediction dataset as reference values. All computations for trimming, calibration, validation and standardization were developed using R. SBC with inoculation was an effective method to transfer calibrations for DA. Validation errors for DA transferred calibrations were about 15% lower than LAB for alfalfa data but 6% greater for grass data. For SCIO after DOP spectral adjustment, predicting errors were slightly greater than LAB for both data sets, while prediction errors with DLP were two to three times greater than LAB even after inoculation. PDS created spectral artifacts in the spectra of all three portables, which then resulted in large validation errors. Using LAB predictions as reference values was suitable only for DA, while DLP and DA had large prediction errors. This study showed that calibration sharing between a benchtop and portable instruments is challenging, but possible depending on the portable technologies and the transfer method. Spectral bias correction plus inoculation was the best method to transfer multivariate models for the forage components’ prediction from LAB to handhelds, particularly for DA. Application of DOP was beneficial for SCIO to successfully maintain performance of the original calibration, while for DLP the prediction models were not accurate. Additional studies are necessary to verify these transferring techniques can also be applied to fresh forages, allowing an easier and extended implementation of NIR analysis directly in fields.
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