Identification of fiber added to semolina by near infrared (NIR) spectral techniques

Badaró, Amanda Teixeira; Morimitsu, Fernanda Lie; Ferreira, Amanda Rios; Clerici, Maria Teresa Pedrosa Silva; Barbin, Douglas Fernandes

doi:10.1016/j.foodchem.2019.03.057

Cited by 58 publications

(38 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Additionally, some peaks found in the region of VIS-NIR at 900–970 nm are reported to be associated with overlapped peaks of starch, cellulose, sucrose, and water, and in particular, peaks from 900 nm to 920 nm are reported to correlate with starch and cellulose [ 28 , 29 ]. Regarding the NIR region, peaks generally represent the constituents of water and vitamin C. It has been reported that the peaks with ranges of 900–1000 nm and 1400–1500 nm are peaks corresponding to the constituent of water [ 30 , 31 ]. Furthermore, peaks at 850, 1000, 1210, 1360, 1460, 1580, 1650 nm have been reported to correlate with vitamin C in powdered mixtures and solutions [ 31 ], as well as in spectra acquired with HIS on whole rocket leaves [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

Comparison Performance of Visible-NIR and Near-Infrared Hyperspectral Imaging for Prediction of Nutritional Quality of Goji Berry (Lycium barbarum L.)

Fatchurrahman

Nosrati

Amodio

et al. 2021

Foods

View full text Add to dashboard Cite

The potential of hyperspectral imaging for the prediction of the internal composition of goji berries was investigated. The prediction performances of models obtained in the Visible-Near Infrared (VIS-NIR) (400–1000 nm) and in the Near Infrared (NIR) (900–1700 nm) regions were compared. Analyzed constituents included Vitamin C, total antioxidant, phenols, anthocyanin, soluble solids content (SSC), and total acidity (TA). For vitamin C and AA, partial least square regression (PLSR) combined with different data pretreatments and wavelength selection resulted in a satisfactory prediction in the NIR region obtaining the R2pred value of 0.91. As for phenols, SSC, and TA, a better performance was obtained in the VIS-NIR region yielding the R2pred values of 0.62, 0.94, and 0.84, respectively. However, the prediction of total antioxidant and anthocyanin content did not give satisfactory results. Conclusively, hyperspectral imaging can be a useful tool for the prediction of the main constituents of the goji berry (Lycium barbarum L.).

show abstract

Section: Resultsmentioning

confidence: 99%

Comparison Performance of Visible-NIR and Near-Infrared Hyperspectral Imaging for Prediction of Nutritional Quality of Goji Berry (Lycium barbarum L.)

Fatchurrahman

Nosrati

Amodio

et al. 2021

Foods

View full text Add to dashboard Cite

show abstract

“…NIRS was mainly controlled by some typical bands of water (5,100 and 7,000 cm −1 as first vibrations of OH; 8,100 and 10,000 cm −1 as second vibrations of OH) and hydrogen bonds, as they could give a strong overtone and combination (Davies, 2002; Ghosh & Jayas, 2009). The absorption bands from 5,000 to 4,000 cm −1 were attributed to amide (Bruun, Holm, Hansen, & Jacobsen, 2006) and NH combination bands, which was characteristic of proteins (Badaró, Morimitsu, Ferreira, Clerici, & Barbin, 2019). The aliphatic series of CH (5,560 and 8,300 cm −1 , respectively, as first and second vibration of bending) and amines (1,550 and 1,640 cm −1 as the bands of amine I; 3,300 cm −1 as the band of amine II) also played an important role (Workman & Workman, 2007).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of quality changes in big‐eye tuna (Thunnus obesus) based on near‐infrared reflectance spectroscopy (NIRS) and low field nuclear magnetic resonance (LF‐NMR)

Lan¹,

Liu

et al. 2020

J Food Process Engineering

View full text Add to dashboard Cite

The purpose of this study was to investigate the effect of temperature fluctuations on quality of big‐eye tuna (Thunnus obesus). In this work, different logistical processes, including cold chain (CK) and cold chain breaks (L1, L2), were simulated. Near‐infrared spectroscopy (NIRS) and low‐field nuclear magnetic resonance (LF‐NMR) were used for determination the quality changes. Meanwhile, water holding capacity (WHC), water activity (aw), sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE), and scanning electron microscope (SEM) were measured. The results indicated that WHC dropped more than 20% in L2 group and the degradation rate of protein was accelerated with the increase of temperature for 216 hr. The curve of NIRS was showed the certain changing trends and its absorbance value showed a downward tendency, which had a decrease from 0.7 at 192 hr to CK > 0.4, L1 ≈ 0.4, L2 < 0.4 at 216 hr. The NIRS further exhibited the changing trend of freshness, which consistent with the results of WHC, SDS‐PAGE and SEM. LF‐NMR showed that immobilized water (T22) decreased while free water (T23) increased during processes. The results were coincidence with other indexes. Therefore, NIRS combined with LF‐NMR could effectively reflect the quality changes of big‐eye tuna during circulation.Practical applicationsNear‐infrared spectroscopy (NIRS) and Low‐field nuclear magnetic resonance (LF‐NMR) can be used to estimate the protein change and water migration of fish. This study indicated that NIRS and LF‐NMR could better characterize the quality change of big‐eye tuna during logistical processes. Moreover, this study provided a theory foundation for the application of NIRS and LF‐NMR on aquatic products.

show abstract

“…Modified PLSR models yielded R 2 values of 68.2-96.7% depending on the adulterant concentration. NIR spectroscopy was also used for determining several quality attributes of flours, such as gliadin, gluten, protein, and moisture contents [27][28][29][30]. Badaró et al [18] studied the feasibility of using a portable NIR sensor (900-1700 nm) to detect the presence of different dietary fibres (psyllium, bamboo, cellulose) added to semolina flour to enhance their nutritional profile.…”

Section: Nir Technology Applications In the Food Manufacturingmentioning

confidence: 99%

“…Classification results obtained in this study are comparable to those by previous studies. Badaró et al [28] achieved model prediction accuracies of up to 100% for detecting the presence of different dietary fibres added to semolina flour using NIR. Ziegler et al [45] illustrated that NIR can be used for differetiating between different flours, such as bread, durum, and spelt, with prediction accuracies up to 100%.…”

Section: Effect Of Sensor Height Light Intensity and Pre-processingmentioning

confidence: 99%

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Rady

Fischer

Reeves

et al. 2019

Sensors

View full text Add to dashboard Cite

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.

show abstract

Identification of fiber added to semolina by near infrared (NIR) spectral techniques

Cited by 58 publications

References 12 publications

Comparison Performance of Visible-NIR and Near-Infrared Hyperspectral Imaging for Prediction of Nutritional Quality of Goji Berry (Lycium barbarum L.)

Comparison Performance of Visible-NIR and Near-Infrared Hyperspectral Imaging for Prediction of Nutritional Quality of Goji Berry (Lycium barbarum L.)

Evaluation of quality changes in big‐eye tuna (Thunnus obesus) based on near‐infrared reflectance spectroscopy (NIRS) and low field nuclear magnetic resonance (LF‐NMR)

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Contact Info

Product

Resources

About