A paper presentation about drying of carrot silces.pptx

It is a paper presentation about drying of carrot slices

1. WELCOME TO MY PRESENTATION

2. Paper Presentation on Computer vision-based smart monitoring and control system for food drying: A study on carrot slices Presented By Md. Manik Mia ID: 1707045 MS Student Dept. of FET HAJEE MOHAMMAD DANESH SCIENCE AND TECHNOLOGY UNIVERSITY, DINAJPUR

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4. Drying , most viable process for extending the shelf-life . Drying system: heat recovery, renewable energy-based and/or hybrid-drying technologies; and (ii) implementation of sensors, and machine learning models for efficient product-process monitoring and control. Computer vision (CV) systems: cost-effective, non-invasive solutions for monitoring product’s attributes like appearance, shape, and texture CV systems, implemented as real-time data acquirement tools in-line for quality monitoring, analyses, multivariate modeling, and feedback Drying process estimation: drying curve based approach, thin layer drying models The CV system, used as a component of the smart Monitoring and Control System (SMCS) for real-time data measurements and subsequent model-based monitoring for process end point determination & product quality control. Introduction

5. Implementing a CV system as a component of SMCS in a prototype drier for real-time monitoring of product changes; To develop linear models utilising the in-line product changes (shrinkage) in predicting drying behaviour of samples subjected to common industrial pre-treatments like blanching; To evaluate the performance and robustness advantages of the CV-based shrinkage-dependent solutions benchmarked against classical thin-layer methods for real-time drying monitoring and control. Process analytical technology (PAT) approaches based on quality by design ( QbD ) Why I choose this paper?

6. Materials and Methods

7. Smart Cabinet Dryer

8. Result and Discussion In-line quality parameters Moisture content and drying rates FIG: In-line monitoring of a) dry basis moisture content, MCdb ; b) drying rate, DR; and c) relative area shrinkage, AS along 36 h of drying of control (CNT) and blanched (BL) carrot slices dried at 35 ◦C, 35 % R.H. and ~ 3 m s − 1 air velocity

9. Surface colour changes Fig. In-line monitoring of colour changes a) luminance (L*) and b) hue angle (h) with respect to dry basis moisture content ( MCdb ) along 36 h of drying for control (CNT) and blanched (BL) carrot slices dried at 35 ◦C, 35 % R.H. and ~ 3 m s − 1 air velocity. The arrow (in black, from right to left) denotes the time flow for both L* and h.

10. Computer vision-based moisture prediction models

11. Computer vision-based moisture prediction models Fig. Plots depicting a replication of a) MR prediction using the logarithmic model wit; b) model fit of the linear segmented model, MR vs AS;

12. CONT. FIG. c) MR prediction using the linear segmented model; d) drying rate curve as a function of the MR predicted based using the linear segmented model.

13. CV system and the load cells integrated in the dryer as SMCS, the attributes of product i.e., weight loss, color, and shrinkage & the pre-defined operation variables (temperature, relative humidity, and airflow) were successfully recorded in real-time during the product drying. From the CV system, findings are the change in the shape of the carrot slices measured as area shrinkage (AS) with respect to the drying time; the change in color indicative of the loss in MC(db) with respect to the changes in AS; and the heterogeneity among the samples within the same drying tray owing to the natural variation in the diameter and drying behavior. It can be said that the use of CV and load cell system supplemented with validated shrinkage-dependent model can enable continuous monitoring and risk assessment through prediction and visualization of a specific process–product space. Remarks on the SMCS implementation

14. The salient findings of this study can be outlined as below: the CV system successfully tracked the shrinkage and colour changes of carrot slices during drying irrespective of pre-treatments; the moisture evolution during drying can be effectively predicted by linear-segmented model considering deviations of shrinkage linearity along the drying period by use of the breakpoint (BP); the CV system enabled with shrinkage-based modeling and proactive quality strategy can be an appropriate and feasible approach for a ‘Smart dryer’ capable of real-time monitoring for better process and product control. CONCLUSIONS

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