The occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which in the future might be coupled to machines to optimize the performance of species-specific weed management. Thus, the objective of this study was to use near-infrared (NIR) spectroscopy and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of the three weed species at the vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm−1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden’s index and were analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of preprocessing was performed. It was possible to obtain an accuracy of 99.3%, 98.5%, and 98.7% for ivyleaf morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth], and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA) or partial least-squares regression with discriminant analysis (PLS-DA) can be used to discriminate Ipomoea spp.

In this work, a method based on Raman spectroscopy in combination with Principal Component Analysis (PCA) and Partial Least Square-Discriminant Analysis (PLS-DA) has been developed for the rapid differentiation of heterofermentative related lactobacilli. In a first approach, Lactobacillus kefir strains were discriminated from other species of heterofermentative lactobacilli: Lb. parakefir and Lb. brevis. After this first approach, PCA allowed for a clear differentiation between Lb. parakefir and Lb.brevis. For the first level of discrimination, PCA was performed on the whole spectra and also on delimited regions, defined taking into consideration the loading values. The best regions allowing a clear differentiation between Lb. kefir and non-Lb. kefir strains were found to be: the 1700–1500 cm−1, 1500–1185 cm−1 and 1800–400 (whole spectrum) cm−1 Raman ranges. In order to develop a classification rule, PLS-DA was carried out on the mentioned regions. This method permitted the discrimination and classification of the strains under study in two groups: Lb. kefir and non-Lb. kefir. The model was further validated using lactobacilli strains from different culture collections or strains isolated from kefir grains previously identified using molecular methods. The second approach based on PCA was also performed on the whole spectra and on delimited regions, being the regions 1700–1500 cm−1, 1500–1185 cm−1 and 1185–1020 cm−1, i.e., those allowing the clearest discrimination between Lb. parakefir and Lb. brevis. The results obtained in this work, allowed a clear discrimination within heterofermentative lactobacilli strains, proteins being the biological structures most determinant for this discrimination.