Evaluation of vacuum impregnation as a novel approach for soaking of fried Gulabjamun balls

Sharanabasava,; Ravindra, Menon Rekha; Kumar, Yogesh; Manjunatha, M.; Kumar, G Mahesh; Nath, B. Surendra

doi:10.1007/s13197-019-03734-5

Cited by 2 publications

(3 citation statements)

References 11 publications

(18 reference statements)

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“…Although surgical resection remains the optimal curable treatment, over 80% of patients with unresectable lesions are not eligible for surgery 2 . Recent advances in systemic chemotherapy brought new hope by shrinking the tumors, thus converting unresectable metastases to resectable ones 3 . However, due to the heterogeneity of CRLM, patients may have different chemotherapy responses 4 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based radiomics predicts response to chemotherapy in colorectal liver metastases

Wei

Cheng

et al. 2020

Medical Physics

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Purpose The purpose of this study was to develop and validate a deep learning (DL)‐based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM). Methods In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first‐line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast‐enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10‐based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response‐related clinical factors and the developed DL radiomics signature. A time‐independent validation cohort (n = 48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL‐based model. Results According to RECIST criteria, 131 patients were identified as responders with complete response, partial response, and stable disease, while 61 patients were nonresponders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380–0.599) and 0.558 (95% CI, 0.374–0.741) in the training and validation cohorts, respectively. The DL‐based model provided better performance than the traditional classifier‐based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851–0.955] vs 0.745 [95% CI, 0.659–0.831]; validation: 0.820 [95% CI, 0.681–0.959] vs 0.598 [95% CI, 0.422–0.774]). The combination of DL‐based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897–0.973] in the training cohort and 0.830 [95% CI, 0.688‐0.973] in the validation cohort. Conclusions The developed DL‐based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision‐making in CRLM management.

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Section: Introductionmentioning

confidence: 99%

Deep learning‐based radiomics predicts response to chemotherapy in colorectal liver metastases

Wei

Cheng

et al. 2020

Medical Physics

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“…Kumar and Ravindra (2016) reported on the development of a sub-baric thermal processor (SBTP) for the preparation of fried and soaked dairy products. The standardization of the process protocol for vacuum impregnation of sugar syrup into fried Gulabjamun balls has been highlighted in Sharanabasava et al (2018Sharanabasava et al ( , 2019. A comparative evaluation of the quality and acceptability of Gulabjamun prepared using sub-baric frying and soaking process against the product prepared using conventional processing protocols is presented in this study.…”

Section: Introductionmentioning

confidence: 94%

“…After the elapse of the soaking time, the soaked product was withdrawn from the syrup and used for analysis. For sub-baric soaking of the fried Gulabjamun balls, the following standardised process was followed (Sharanabasava et al 2019). The balls were loaded in perforated trays and the tray was introduced in the soaking unit of the SBTP with a bath of sugar syrup (60 o Brix) in the tank section at the bottom of the unit.…”

Section: Soaking Of Gulabjamunmentioning

confidence: 99%

Comparative evaluation between atmospheric and sub-baric processing of Gulabjamun

Sharanabasava

R²,

S³

et al. 2019

IJDS

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Gulabjamun, a popular Indian dairy sweet, is characterised with a brown crust and a soft, slightly spongy core with a uniform granular texture and a pleasant cooked flavour. Sub-baric processing is a novel technology which uses low temperature, low pressure and minimal exposure to oxygen which ultimately improves the quality parameters of fried foods. An attempt was made to compare conventional and sub-baric processed Gulabjamun in terms of its sensory, physical, chemical and microbial attributes. The processing conditions for frying were 118°C/5min/80kPa vacuum and 140°C/8min (under atmospheric pressure) for sub-baric frying and conventional frying, respectively. The soaking conditions were 60°Brix at 30°C for 5min for sub-baric soaking and 63°Brix/60°C/4 h for conventional soaking. The sub-baric frying of Gulabjamun resulted in reduced oil and moisture content in the final product. Microbial enumeration also indicated a lower count in sub-baric fried product when compared to conventionally fried Gulabjamun. The colour of both samples was comparable while the textural properties were found to be better for sub-baric processed product. Sensory score for overall acceptability based on 9 point hedonic scale were 8.2 and 7.14 for sub-baric processed and conventional product, respectively. Apart from these advantages, sub-baric processing recorded significantly less processing time than the conventional method.

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Evaluation of vacuum impregnation as a novel approach for soaking of fried Gulabjamun balls

Cited by 2 publications

References 11 publications

Deep learning‐based radiomics predicts response to chemotherapy in colorectal liver metastases

Deep learning‐based radiomics predicts response to chemotherapy in colorectal liver metastases

Comparative evaluation between atmospheric and sub-baric processing of Gulabjamun

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