Citrus species and hybrids depicted by near- and mid-infrared spectroscopy

On-site analysis and rapid identification of citrus herbs by miniature mass spectrometry and machine learning

BACKGROUND

Natural medicines present a considerable analytical challenge due to their diverse botanical origins and complex multi-species composition. This inherent complexity complicates their rapid identification and analysis. Tangerine peel, a product of the Citrus species from the Rutaceae family, is widely used both as a culinary ingredient and in traditional Chinese medicine. It is classified into two primary types in China: Citri Reticulatae Pericarpium (CP) and Citri Reticulatae Pericarpium Viride (QP), differentiated by harvest time. A notable price disparity exists between CP and another variety, Citri reticulatae "Chachi" (GCP), with differences being based on the original variety.

METHODS

This study introduces an innovative method using portable miniature mass spectrometry for swift on-site analysis of QP, CP, and GCP, requiring less than a minute per sample. And combined with machine learning to differentiate the three types on site, the method was used to try to distinguish GCP from different storage years.

RESULTS

This novel method using portable miniature mass spectrometry for swift on-site analysis of tangerine peels enabled the characterization of 22 compounds in less than one minute per sample. The method simplifies sample processing and integrates machine learning to distinguish between the CP, QP, and GCP varieties. Moreover, a multiple-perceptron neural network model is further employed to specifically differentiate between CP and GCP, addressing the significant price gap between them.

CONCLUSIONS

The entire analytical time of the method is about 1 minute, and samples can be analyzed on site, greatly reducing the cost of testing. Besides, this approach is versatile, operates independently of location and environmental conditions, and offers a valuable tool for assessing the quality of natural medicines.

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Application of NIR Spectroscopy for the Valorisation of Cork By-Products: A Feasibility Study over the Screening and Discrimination of Chemical Compounds of Interest

Quercus suber is considered a sustainable tree mainly due to its outer layer (cork) capacity to regenerate after each harvesting cycle. Cork bark is explored for several application; however, its industrial transformation generates a significant amount of waste. Recently, cork by-products have been studied as a supplier of bioactive ingredients. This work aimed to explore whether near infrared spectroscopy (NIRS), a non-destructive analysis, can be employed as a screening device for selecting cork by-products with higher potential for bioactives extraction. A total of 29 samples of cork extracts were analysed regarding their qualitative composition. Partial least squares (PLS) models were developed for quantification purposes, and R2P and RER values of 0.65 and above 4, respectively, were obtained. Discrimination models, performed through PLS-DA, yielded around 80% correct predictions, revealing that four out of five of samples were correctly discriminated, thus revealing that NIR can be successfully applied for screening purposes.

Lipid Profile Quantification and Species Discrimination of Pine Seeds through NIR Spectroscopy: A Feasibility Study

Pine seeds are known for their richness in lipid compounds and other healthy substances. However, the reference procedures that are commonly applied for their analysis are quite laborious, time-consuming, and expensive. Therefore, it is important to develop rapid, accurate, multi-parametric, cost-effective and, essentially, environmentally friendly analytical techniques that are easily implemented at an industrial scale. The viability of using near-infrared (NIR) spectroscopy to analyse the seed lipid content and profile of three different pine species (Pinus halepensis, Pinus brutia and Pinus pinaster) was investigated. Moreover, species discrimination using NIR was also attempted. Different chemometric models, namely partial least squares (PLS) regression, for lipid analysis, and partial least square-discriminant analysis (PLS-DA), for pine species discrimination, were applied. In relation to the discrimination of pine seed species, a total of 90.5% of correct classification rates were obtained. Regarding the quantification models, most of the compounds assessed yielded determination coefficients (R²_P) higher than 0.80. The best PLS models were obtained for total fat, vitamin E, saturated and monounsaturated fatty acids, C20:2, C20:1n9, C20, C18:2n6c, C18:1n9c, C18 and C16:1. Globally, the obtained results demonstrated that NIR spectroscopy is a suitable analytical technique for lipid analysis and species discrimination of pine seeds.

Using attenuated-total-reflection Fourier-transformed spectroscopy to reveal molecular structural differences among willow (Salix spp.) foliage cultivars in relation to their potential as fodders

BACKGROUND

Willow trees represent a suitable species for the development of agroforestry systems, integrating bioenergy and animal feed production. However, there is a lack of information regarding the suitability of leaves and stems, considered a bioenergy by-product, as animal feed. The aim of this study was the employment of attenuated total reflectance Fourier transform infrared spectroscopy (550-4000 cm^-1 ) to investigate differences in the nutrient molecular structure profile of leaves and stems of selected willow cultivars to understand their utility for ruminant nutrition.

RESULTS

Univariate analysis of variance of leaves showed lower intensities of cellulosic compounds and higher of protein in comparison with stems, which suggests higher leaf dry matter and protein digestibility. Spectral analyses revealed differences in both plant parts between Salix cv. Terra Nova and Salix cv. Beagle, cv. Resolution, and cv. Olof. The higher α-helix to β-sheet ratio, which is related to a higher protein digestibility, was in correlation with the lower content of condensed tannins. Principal component and agglomerative hierarchical cluster analyses showed significant discrimination among willow cultivars in the cellulosic, structural carbohydrate, and amide regions, whereas differences were less evident for total carbohydrate and lipid-related regions.

CONCLUSION

The application of attenuated total reflectance Fourier transform infrared molecular spectroscopy is an effective tool to rapidly identify spectral features related to the nutritional composition of willow foliage and to discriminate between cultivars and parts of the plant. This information would be useful to optimize the use of willow fodders in agroforestry systems. © 2021 Society of Chemical Industry.

Reading light: leaf spectra capture fine-scale diversity of closely related, hybridizing arctic shrubs

Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine-scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400-2400 nm) can distinguish species and detect fine-scale population structure and phylogenetic divergence - estimated from genomic data - in two co-occurring, hybridizing, ecotypically differentiated species of Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. Reflectance spectra captured genetic variation and accurately distinguished fine-scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine-scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally-based biodiversity assessments become more prevalent.

Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces

Olive pomace, a wet semi-solid paste that remains after olive oil extraction, is a major waste of the process and its recovery is mandatory due to its phytotoxicity when rejected directly into the soil. Innovative applications have been studied, but simple and reliable methods that fulfil the gap between the recovery of compounds and their use by industries (contributing to the sustainability and circular economy of the chain) still need to be explored. In this work, four monocultivar olive pomaces (Arbequina, Arbosana, Oliana, and Koroneiki) were studied regarding their nutritional composition, fatty acids and vitamin E profiles, total phenolic and flavonoid contents, antioxidant activity, and Fourier Transform Near Infrared and Mid Infrared spectra. Principal Component Analysis and Partial Least Square Discriminant Analysis were used to discriminate samples. Arbosana pomace presented the highest total fat (15%, dw) and vitamin E contents (1.4 mg/g of oil), being α-tocopherol the main vitamer in all samples. Koroneiki pomace was the richest in phenolic compounds (9 g gallic acid eq./kg). The major fatty acid was oleic acid. Stearic acid, linoleic acid, and FRAP levels differed significantly among cultivars. NIR spectra showed differences in all spectral regions (best separation from 6504 to 5389 cm^-1 and 4961 to 4035 cm^-1), while MIR spectra presented differences only in some spectral regions. The results showed that Near Infrared spectroscopy together with Principal Component Analysis is a powerful tool to discriminate olive pomace cultivars, with ability to be used in an industrial context.

A review on the application of vibrational spectroscopy to the chemistry of nuts

Nuts are highly appreciated due to their nutritional relevance and flavour, being the source of many desirable and healthy compounds as polyunsaturated fatty acids and antioxidants. Their characterization became the target of many studies in the last years through conventional analytical techniques as chromatographic ones. Due to the limitations associated to these techniques, as time, cost and environmental concerns, spectroscopic techniques have been increasingly pointed as reliable alternatives. Either applied to raw materials quality control or to more complex process, as industrial in-line monitoring, spectroscopic techniques, namely vibrational spectroscopy, are gathering strong acceptance. This paper presents a review on the application of vibrational spectroscopy, infrared and Raman, to nuts characterization. Estimates of several qualitative and quantitative parameters, origin authentication and/or adulteration in almonds, peanuts, pine nuts, hazelnuts, walnuts, Brazil nuts, cashews, chestnuts and pistachios will be covered. Advantages and limitations of these techniques and future trends will also be discussed.