Pedometric tools for classification of southwestern Amazonian soils: A quali-quantitative interpretation incorporating visible-near infrared spectroscopy

The southwestern region of the Amazon has great environmental variability, presents a great complexity of pedoenvironments due to its rich variability of geological and geomorphological environments, as well as for being a transition region with other two Brazilian biomes. In this study, the use of pedometric tools (the Algorithms for Quantitative Pedology (AQP) R package and diffuse reflectance spectroscopy) was evaluated for the characterization of 15 soil profiles in southwestern Amazon. The AQP statistical package—which evaluates the soil in-depth based on slicing functions—indicated a wide range of variation in soil attributes, especially in the superficial horizons. In addition, the results obtained in the similarity analysis corroborated with the description of physical, chemical components and oxide contents in-depth, aiding the classification of soil profiles. The in-depth characterization of visible-near infrared spectra allowed inference of the pedogenetic processes of some profiles, setting precedents for future work aiming to establish analytical strategies for soil classification in southwestern Amazon based on spectral data.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

Evaluation of Kraft pulp yield and syringyl/guaiacyl ratio from standing trees (Eucalyptus camaldulensis, E. urophylla, Leucaena leucocephala and Casuarina junghuhniana) using portable near infrared spectroscopy

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.

Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy

Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r2 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r2 0.98 ± 0.01. Thus, the model accuracy ±10–30 % is well within the standard safety margins.

Near infrared spectroscopy and aquaphotomics evaluation of the efficiency of solar dehydration processes in pineapple slices

Parallel transformation tests on pineapple slices using two micro drying plants (M1 and M2) operating with solar energy were carried out. Method M1 consisted of an active fan at the top, whose ventilation rate depended on the internal temperature. Method M2 had a continuously working fan at the bottom. The dehydration performance of these two micro-plants was compared by collecting spectra from pineapple slices in reflectance mode (900–1600 nm) at three different times: (0) process start, (1) during the process [48 h] and (2) process end [56 h]. Simultaneously, dry matter, titratable acidity (SH°), pH and aw (water activity) were measured. For these parameters, significant differences ( p < 0.05) were detected between the fresh (t = 0) and the dried product (t = 56). Near infrared (NIR) spectra analysis was carried out according to previously published methods. Spectral data in the wavelength region from 1300 to 1550 nm underwent statistical processing to perform aquaphotomics evaluation and chemometrics methods such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The Aquagrams highlighted differences among fresh, half-dried and dried slices where water molecules were highly organized between the water matrix coordinates C1 to C3 at t = 0 and C2 to C6 for the other evaluated times. The PCA could explain about 98% of the total variance in the PC1–PC3 scores plot. And the additional LDA classified the NIR spectra with an accuracy of 100, 98 and 83% for t = 0, t = 56-M1 and t = 56-M2, respectively. Such preliminary results suggest the applicability of Aquaphotomics and chemometrics for the continuous monitoring of fruit drying processes using an adequate NIR probe. Further experiments are already in progress.

Nondestructive evaluation of the biogenic amine in crayfish (Prokaryophyllus clarkii) by nearinfrared spectroscopy

Biogenic amines are a group of nitrogen substances and widely adopted to assess the food safety, especially for the aquatic products. In China, crayfish ( Prokaryophyllus clarkii) have become one of the most famous aquatic products and form a complete industrial value chain. To ensure the safety of the crayfish industrial chain, a rapid and nondestructive method for determining the biogenic amines of crayfish by nearinfrared spectroscopy coupled with chemometrics was proposed in this study. The quantitative models of histamine, tyramine, cadaverine, and putrescine were built by using the partial least squares (PLS) regression. The spectral preprocessing and the wavelength selection methods were adopted to optimize the models. For histamine, cadaverine, and putrescine in peeled or whole tails, reasonable quantitative results can be obtained by using the optimized models; the coefficient of determination (r2) are 0.88 and 0.90, 0.88 and 0.91, 0.89, and 0.84, respectively. As for tyramine in peeled or whole tails, the results are acceptable and the coefficient of determination (r2) is 0.83 and 0.74, respectively.

Single kernel sorting of high and normal oleic acid peanuts using near infrared spectroscopy

Peanuts are known to contain nutrients that deliver cardiovascular and health benefits. One such compound is oleic acid, an omega-9 monounsaturated fatty acid, which occurs naturally in peanuts in the concentration range 40–55% m/m, while some varieties are known to contain oleic acid above 75% m/m. These high oleic peanuts have been shown to have cardiovascular health benefit by lowering lipid levels. Breeders are therefore interested in selecting for peanuts with high oleic acid content in a rapid, non-destructive manner. Near infrared spectra acquired on single peanut kernels was used to classify the kernels as either high oleic content or normal, low oleic content, by means of partial least squares discriminant analysis with an overall error rate in classification of 3.3%.

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

Geographical identification of Italian extra virgin olive oil by the combination of near infrared and Raman spectroscopy: A feasibility study

The potential of the combination of near infrared (NIR) spectroscopy and Raman spectroscopy to differentiate Italian and Greek extra virgin olive oil (EVOO) by geographical origin was evaluated. Near infrared spectroscopy and Raman fingerprints of both study groups (extra virgin olive oil from the two countries) were pre-processed, merged by low-level and mid-level data fusion strategies and submitted to partial least-squares discriminant analysis. The classification models were cross-validated. After low-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 93.9% accuracy, while sensitivity and specificity were 77.8% and 100%, respectively. After mid-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 97.0% accuracy, while sensitivity and specificity were 88.9% and 100%, respectively. In this preliminary study, improved discrimination of Italian extra virgin olive oils was achieved by the synergism of near infrared spectroscopy and Raman spectroscopy as compared to the discrimination obtained by the separate laboratory techniques. This pilot study shows encouraging results that could open a new avenue for the authentication of Italian extra virgin olive oil.

Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification

The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rhizobacteria at the genus and species level. To develop the method, bacteria were inoculated on an agar medium and analyzed by near infrared (NIR) and ultraviolet-visible-NIR (UV-Vis-NIR) spectroscopy. Artificial neural network models applied to the UV-Vis-NIR spectra correctly identified the genus (species) of 70% (63%) of the lactic acid bacteria and 67% of the rhizobacteria on an independent prediction set of unknown bacterial strains. These results demonstrate the potential of UV-Vis-NIR spectroscopy to identify bacteria directly on agar plates.