Predicción no destructiva de sólidos solubles, acidez y pH de aguaymanto (Physalis peruviana L) mediante propiedades dieléctricas en el rango de microondas

La investigación tuvo como objetivo predecir los sólidos solubles, acidez y pH de los frutos de aguaymanto (Physalis peruviana L.) mediante la técnica de espectroscopía dieléctrica en el rango de microondas. Las propiedades dieléctricas (constante dieléctrica – Ɛ’) de los frutos de aguaymanto se midieron en una escala logarítmica de 0.5 a 9 GHz a 20 ºC, mediante una sonda coaxial abierta conectado a un analizador vectorial de redes, además, se determinó los sólidos solubles, acidez y pH. Los resultados mostraron que los valores medios de los espectros de la constante dieléctrica de los frutos de aguaymanto disminuyó a medida que aumentaba la frecuencia de 0.5 a 9 GHz, asimismo, los valores de sólidos solubles aumentaron de 13.493 a 14.850 ºBrix, la acidez disminuyo de 2.842 a 2.120 y el pH aumentó de 3.468 a 3.663. Los mejores modelos de predicción para los sólidos solubles a 915 MHz con R2=0.7760 y RECM=0.5185, la acidez a 5800 MHz con R2=0.4790 y RECM=0.2391 y el pH a 2450 MHz con R2=0.9994 y RECM=0.0221

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400–1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries