Browsing by Subject "Near infrared"

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    A volatilomic approach using ion mobility and mass spectrometry combined with multivariate chemometrics for the assessment of lemon juice quality
    (Elsevier, 2024-11-09) Giménez Campillo, Claudia; Arroyo Manzanares, Natalia; Campillo Seva, Natalia; Díaz García, Miriam Cristina; Viñas López-Pelegrin, Pilar; Química Analítica
    Lemon (Citrus limon (L.) Burm.) is a citrus fruit known for its high nutritional value and potent antioxidant activity. Lemon juice, obtained by squeezing the fruit, is widely used in the kitchen for its acidic taste to flavour dishes and drinks. It has also been attributed with various medicinal properties to treat conditions such as sore throat, fever, rheumatism and hypertension. Ensuring the quality and safety of lemon juice, as well as its geographical origin, is not easy due to the scarcity of analytical methods available for this purpose, which makes it difficult to detect adulterations. To meet this challenge of testing the authenticity and safety of lemon juice, multiple physicochemical parameters need to be evaluated, which is expensive and time-consuming, so it is of great interest to develop an alternative simple method. In this research, two alternative analytical methods were developed and optimized for the analysis of lemon juice samples based on headspace gas chromatography coupled to both mass spectrometry (HS-GC-MS) or ion mobility spectrometry (HS-GC-IMS). These new methods were compared with the method currently used in the food industry for quality control of juices, which is Fourier transform near infrared spectroscopy (FT-NIR). A total of 159 samples belonging to different lemon varieties were analysed by measuring the physicochemical parameters, FT-NIR spectra and fingerprinting of the juice samples based on the total volatile compounds profile by GC-MS and GC-IMS. Partial least squares (PLS) regression models were then constructed and all models were validated by paired tests with the values measured by the reference chemical methods. The models developed confirm that both HS-GC-MS and HS-GC-IMS methods are viable alternatives for predicting physicochemical parameters and ensuring lemon juice quality. Finally, the data were used to build chemometric models using orthogonal partial least squares discriminant analysis (OPLSDA) to distinguish lemon juices according to the lemon variety used in their manufacture. Very promising models were obtained with the HS-GC-MS and HS-GC-IMS data, suggesting the potential use of the volatile profile for lemon variety confirmation. Consequently, fingerprinting represents an alternative proposal to the conventional method applied in the food industry based on the use of chemical reference parameters or the use of the NIR technique.
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    Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy
    (Elsevier , 2022-11-01) Pourdarbani, Razieh; Sabzi, Sajad; Rohban, Mohammad H.; García Mateos, Ginés; Paliwal, Jitendra; Molina‑Martínez, José Miguel; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    This study focuses on the spectrochemical estimation of pH and titratable acidity (TA) of apples of Fuji variety at different stages of ripening. A novel approach is proposed for near-infrared (NIR) spectral analysis using hybrid machine learning methods that combine artificial neural networks (ANN) and metaheuristic algorithms. One hundred twenty samples were collected at three ripening stages and spectral data within two bands of NIR were extracted from each sample to predict the acidity properties. Alternatively, the 4 most effective wavelengths were also selected using a hybrid of ANN and the cultural algorithm. The experimental results prove that the models using spectral bands and the 4 most effective wavelengths are comparable, with a correlation coefficient, R, of 0.926 for the prediction of pH and 0.925 for TA using spectral bands, while for the second approach the R obtained were 0.924 and 0.920 for pH and TA, respectively. The models could not accurately predict extremely high or low pH and TA values, due to the clusters that formed after regression. However, for a classification problem in low/high acidity, both approaches were able to achieve a high accuracy of 100% for pH and 99.2% for TA.