Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and...

Apple ring rot caused by Botryosphaeria dothidea can cause fruit decay during the growth and storage stages of apple fruit. Understanding the infection process and cellular defense response at the cellular micro-level holds immense importance in the field of prevention and control. Consequently, there is a pressing need to develop suitable chemical imaging analysis methods. Here we proposed a label-free, high-throughput imaging method for cellular investigation of apple fruit ring rot infected by Botryosphaeria dothidea, based on confocal Raman microspectroscopic imaging technology combined with multivariate curve resolution-alternating least squares algorithm (MCR-ALS). We conducted Raman measurements on every apple fruit and obtain an image cube. This cube was then unfolded into an augmented matrix in a column-wise manner. We proceeded with simultaneous MCR-ALS analysis, resolving the single-substance spectrum and concentration profile from the mixed signals. Lastly, the accurate and pure molecular imaging of low methoxyl pectin, high methoxyl pectin, cellulose, lignin, and phenols were realized by refolding the resolved concentration data to construct the composition image. Thereafter, we realized the study of the spatial-temporal changes distribution of the above substances in the cuticle and cell wall of green and red apples at different stages of infection. The imaging method proposed in this paper is expected to provide a chemical imaging strategy for studying pathogen infection process and fruit defense response at the cellular level. In addition, by utilizing a fiber-optic probe near-infrared reflection spectrometer in conjunction with machine learning, we developed a rapid and non-destructive classification method. This method allows for the timely identification of apples exhibiting early infection by Botryosphaeria dothidea. Notably, both principal component analysis-quadratic discriminant analysis and support vector machine achieved a classification accuracy of 100%.