Cristina Petisco

Potential of near infrared transflectance spectroscopy to detect adulteration of Irish honey by beet invert syrup and high fructose corn syrup

Near infrared (1100–2498 nm) transflectance spectroscopy was used to detect beet invert syrup (BI) and high fructose corn syrup (HFCS) adulterants in artisanal Irish honey. The sample set investigated comprised authentic (n = 83), BI-adulterated (n = 56) and HFCS-adulterated (n = 40) honeys. Soft independent modelling of class analogy was used to classify honeys as authentic or adulterated while partial least squares regression (PLS1) was used to predict the adulteration level. Spectral data were investigated in three forms: raw, after multiplicative scatter correction and after second derivative transformation. The best classification model was obtained using raw spectral data. The preferred models for prediction of percentage adulteration involved PLS1 of multiplicative scatter corrected spectra (adulteration with BI) and second derivative transformation (adulteration with HFCS). The present study has demonstrated that near infrared spectroscopy could be used as a rapid screening tool for detection of BI and HFCS adulteration in Irish honey.

Ash and Mineral Contents in Leaves of Woody Species: Analysis by Near‐Infrared Reflectance Spectroscopy

Abstract Near‐infrared reflectance spectroscopy (NIRS) was evaluated for its effectiveness to determine ash and mineral concentrations [potassium (K), magnesium (Mg), copper (Cu), iron (Fe), and zinc (Zn)], in a total of 182 leaf samples of 17 woody species located in the central‐western region of the Iberian Peninsula. Chemical analysis revealed great variability in all leaf mineral elements. This variability was mainly related to differences in leaf habit (deciduous versus evergreen) and to differences in mean leaf longevity and among leaf age classes within evergreen species. A set of samples including all 17 species and leaf age classes was used to develop the calibration equations using multiple linear regression (MLR) and partial‐least squares regression (PLSR). The set of samples that did not enter in the calibration was used for external validation. In general, the most satisfactory results were obtained using PLSR and derivative transformations. Despite the strong heterogeneity of the samples included in the study, the results showed that NIRS can be employed as an effective tool, alternative to the more time‐consuming standard methods. The best predictive model was obtained for ash content. Models with acceptable accuracy were obtained in the prediction of K and Mg contents. However, their applicability for the determination of trace elements was more limited.

Direct classification of related species of fungal endophytes (Epichloë spp.) using visible and near-infrared spectroscopy and multivariate analysis

The aim of this work was to investigate the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for the classification of three morphologically similar species of fungal endophytes of grasses. Vis-NIR spectra (400-2498 nm) from 34 isolates of Epichloë sylvatica, 32 of Epichloë typhina and 38 of Epichloë festucae were recorded directly from fresh mycelium growing in potato dextrose agar plates. Multivariate procedures applied to the spectral data were discriminant modified partial least squares regression, soft independent modelling of class analogy and discriminant partial least squares regressions (PLS1, PLS2). Several types of data pretreatments were tested to develop the classification models. The best predictive models were achieved with PLS2 analysis; with this method, 90% of E. typhina and 100% of E. festucae and E. sylvatica external validation samples were successfully classified. These results show the potential of Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying morphologically similar species of filamentous fungi.

A spectroscopy approach to the study of virus infection in the endophytic fungus Epichloë festucae

BackgroundIn this work we propose a rapid method based on visible and near-infrared (Vis-NIR) spectroscopy to determine the occurrence of double-stranded RNA (dsRNA) viruses in Epichloë festucae strains isolated from Festuca rubra plants. In addition, we examined the incidence of infections by E. festucae in populations of F. rubra collected in natural grasslands of Western Spain.MethodsVis-NIR spectra (400-2498 nm) from 124 virus-infected and virus-free E. festucae isolates were recorded directly from ground and freeze-dried mycelium. To estimate how well the spectra for uninfected and infected fungal samples could be differentiated, we used partial least-squares discriminant analysis (PLS1-DA) and several data pre-treatments to develop calibration models.ResultsApplying the best regression model, obtained with two sampling years and using standard normal variate (SNV) combined with first derivative transformation to a new validating data set (42 samples), we obtained a correct classification for 75% of the uninfected isolates and up to 86% of the infected isolates.ConclusionsThe results obtained suggest that Vis-NIR spectroscopy is a promising technology for detection of viral infections in fungal samples when an alternative faster approach is desirable. It provides a tool adequately exact and more time- and cost-saving than the conventional reference analysis.

Quantitative Analysis of Chlorophyll and Protein in Alfalfa Leaves Using Fiber‐Optic Near‐Infrared Spectroscopy

The performance of near‐infrared (NIR) spectroscopy as a rapid technique for the estimation of chlorophyll and protein contents in alfalfa (Medicago sativa L.) was investigated. A fiber‐optic probe was employed directly on a total of 198 fresh leaves to measure spectra between 1100 and 2200 nm. Partial least squares (PLS) regression models were developed with a calibration set of 120 samples spanning a concentration range of 5.20–158.5 for the chlorophyll content index (CCI), 0.39–4.60 mg g−1 (fresh weight) for the chlorophyll extracted with dimethylsulfoxide (DMSO), and 9.92–45.32% (dry matter) for protein content. The models obtained were validated with 78 independent samples. Standard errors of prediction of 12.49 were obtained for the CCI, 0.24 mg g−1 for DMSO‐extracted chlorophyll, and 3.27% for the protein content. These results support the use of NIRS equipped with a fiber‐optic probe to monitor and assess the composition and quality of forages in a nondestructive way.