Saffron as a natural food colorant and its applications

Ahmed, Abdul Bakrudeen Ali; Taha, Rosna Mat; Anuar, Nordiyanah; Elias, Hashimah; Abdullah, Sakinah; Khan, Asif; Lobo, Vijay; Raman, Vidhyavathi

doi:10.1016/b978-0-12-821219-6.00006-3

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…The experimental adulteration was done with whole saffron stigma filaments (8 samples) and partially powdered stigma filaments (5 samples). The adulterated samples are listed below (Table 1), including a 14th "saffron" Spanish Safrante sample that was sourced without certification from a street market that was easily determined as adulterated (unknown substance) through observation (saffronsystems) and a commonly used water dilution test (Shukla and Iqbal, 2015;Ahmed et al, 2021).…”

Section: Sourcing and Preparation Of Saffron And Non-saffron Samplesmentioning

confidence: 99%

Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization

Elaraby,

Ali,

Zhou

et al. 2023

Front. Hortic.

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IntroductionSaffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron.MethodsIn this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose.ResultsThe observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency.DiscussionA discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.

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Section: Sourcing and Preparation Of Saffron And Non-saffron Samplesmentioning

confidence: 99%

Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization

Elaraby,

Ali,

Zhou

et al. 2023

Front. Hortic.

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“…For centuries, it has been a staple in traditional medicine and culinary practices (Jyotirmayee et al 2023) [12] . Turmeric is renowned for its brilliant golden-yellow hue and unique flavour, which imparts richness and warmth to a wide range of dishes (Ahmed et al 2021) [1] . The active compound responsible for both the colour and various health benefits of turmeric is known as curcumin (Chattopadhyay et al 2004) [7] .…”

Section: Introductionmentioning

confidence: 99%

Influence of graded levels of inorganic with organics and biofertilizers on yield, quality and economics of turmeric (Curcuma longa L.)

Kumar,

Chanchan,

et al. 2024

Int. J. Adv. Biochem. Res.

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The study was carried out at horticulture experimental field of Central Agricultural University, Iroisemba, Imphal West, Manipur, during 2021-2022. Thirteen treatments and three replications were included in the RBD design of the experiment. Biofertilizers and organic manures are two more sustainable and alternative sources of nutrients. When biofertilizers and organic manure are mixed, the growth is affected by increased root biomass; total root surface increases nutrient absorption and yield by using less natural energy sources. The combination of organic manures viz., compost, vermicompost, mustard cake and neem cake and BF (biofertilizers) viz., nitrogenous (Azotobacter chroococcum), phosphate solubilizing bacteria (Bacillus polymixa) and potassic mobilizing (Fraturia aurantea) and three levels (100%, 75% and 50%) of NPK were included in this experiment along with recommended dose of NPK (150:60:150 kg/ha). Result of the present experimentation disclosed that the application of Vermicompost + NPK 100% + BF recorded significant value for yield per plot (15.40 kg 3.0 m -2 ), yield (38.50 t ha -1 ), curcumin content (6.32%) and oleoresin content (13.74%). The application of Vermicompost + NPK 100% + BF recorded maximum gross return (Rs. 1155000/ha), net return (Rs. 792252.04/ha) and highest B:C ratio (2.24) followed by Compost + NPK 100% + BF (2.11).

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“…Saffron comprises bioactive ingredients such as safranal, crocin, crocetin, picrocrocin, kaempferol, and flavonoids, which can be extracted and purified using diverse methodologies. 7 These ingredients showed their deserved antioxidant, anti-inflammatory, antidepressant activities, and antitumor. 8 Moreover, saffron boasts therapeutic characteristics and has been employed in traditional medicine and pharmacopeia.…”

Section: Introductionmentioning

confidence: 99%

Saffron Nanoencapsulation (Crocus sativus) and Its Role in Food Science: Types and Techniques

Borjizadeh,

Ahari,

Özdal

et al. 2024

ACS Food Sci. Technol.

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The encapsulation of saffron holds great importance as it contributes to the improvement of its stability, purity, and targeted release of its bioactive components. The purpose of this review is to provide comprehensive information about the main methods for saffron encapsulation, types of encapsulation, and the current status of its patents in the field of food science. Numerous studies have been conducted to encapsulate saffron and its ingredients within micro/nanoparticles. According to them, nanoliposomes, polymeric nanoparticles, nanodroplets, and nanofibers have been employed for saffron encapsulation. These nanocarriers were synthesized using ultrasonication, emulsification, spray drying, freeze-drying, electrospinning, chemical synthesis, and ionic gelation. The size, stability, bioavailability, and release kinetics were crucial for their specific application. In general, ionic gelation, ultrasonication, and spray drying were the most popular methods for saffron encapsulation, respectively. Only a limited number of patents exist about nanosaffron for food science applications.

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Saffron as a natural food colorant and its applications

Cited by 9 publications

References 51 publications

Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization

Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization

Influence of graded levels of inorganic with organics and biofertilizers on yield, quality and economics of turmeric (Curcuma longa L.)

Saffron Nanoencapsulation (Crocus sativus) and Its Role in Food Science: Types and Techniques

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