Background and Aim The compactness of a grape bunch can be a significant trait in determining tablegrape and wine quality. The Organisation Internationale de la Vigne et du Vin developed the most widely used visual method to assess bunch compactness from loose (1) to tight (9). This method, however, requires training and relies on manual measurements and is, thus, subject to bias and error. The aim of this study was to test the feasibility of multi‐perspective imaging analysis combined with multivariate modelling to predict grape bunch compactness. Such a method has the potential to be rapid, automated and objective. Methods and Results Vitis labruscana cv. Kyoto grape bunches were collected from three vineyards over two consecutive seasons and imaged with a multi‐perspective imaging system, which sensed mass and imaged the surface of the bunch from three perspectives using mirror reflection. Bulk density of the grape bunch was linearly related to compactness (correlation coefficient 0.679). The morphological features of grape bunches and their derivative variables were digitised using 23 image processing descriptors and were regressed with the measured compactness using multivariate data analysis, including partial least squares, multiple linear regression and principal component regression. The partial least squares model was the best performed, predicting bunch compactness with a correlation coefficient of prediction (rp) of 0.8481 as well as root mean squared error of prediction of 1.2287. Conclusions Multi‐perspective imaging combined with image processing and multivariate data analysis can assess the compactness of grape bunches. Significance of the Study The performance of this multi‐perspective imaging method could be developed to automate the postharvest assessment of the compactness of grape bunches.

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The chemical and physical properties of instant whole milk powder (IWMP), such as morphology, protein content, and particle size, can affect its functionality and performance. Bulk density, which directly determines the packing cost and transportation cost of milk powder, is one of the most important functional properties of IWMP, and it is mainly affected by physical properties, e.g., morphology and particle size. This work quantified the relationship between morphology and bulk density of IWMP and developed a predictive model of bulk density for IWMP. To obtain milk powder samples with different particle size fractions, IWMP samples of four different brands were sieved into three different particle size range groups, before using the simplex-centroid design (SCD) method to remix the milk powder samples. The bulk densities of these remixed milk powder samples were then measured by tap testing, and the particles’ shape factors were extracted by light microscopy and image processing. The number of variables was decreased by principal component analysis and partial least squares models and artificial neural network models were built to predict the bulk density of IWMP. It was found that different brands of IWMP have different morphology, and the bulk density trends versus the shape factor changes were similar for the different particle size range groups. Finally, prediction models for bulk density were developed by using the shape factors and particle size range fractions of the IWMP samples. The good results of these models proved that predicting the bulk density of IWMP by using shape factors and particle size range fractions is achievable and could be used as a model for online model-based process monitoring.

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The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.