PMID- 34814005
OWN - NLM
STAT- Publisher
LR  - 20240220
IS  - 1090-2414 (Electronic)
IS  - 0147-6513 (Linking)
VI  - 228
DP  - 2021 Nov 20
TI  - Potential of Vis-NIR to measure heavy metals in different varieties of 
      organic-fertilizers using Boruta and deep belief network.
PG  - 112996
LID - S0147-6513(21)01108-8 [pii]
LID - 10.1016/j.ecoenv.2021.112996 [doi]
AB  - The quick identification of heavy metals is of major importance and is beneficial 
      for controlling the fertilizer production process in the fertilizer industries. 
      This work aimed to use visible and near-infrared spectroscopy (Vis-NIR), Boruta, 
      and deep learning to establish rapid heavy metals screening methods. Boruta 
      algorithm was used to extract appropriate wavelengths, and a deep belief network 
      (DBN) was computed to determine the amounts of various heavy metals such as 
      chromium (Cr), cadmium (Cd), lead (Pb), and mercury (Hg) for both the entire and 
      selected wavelengths. To assess the model, coefficient of determination (R(2)), 
      root mean squared error (RMSE), and residual prediction deviation (RPD) were used 
      to calculate the reliability of the model. The results of the selected 
      wavelengths were excellent and much higher than the full wavelengths with 
      R(2)p = 0.96, RMSEP = 0.2017 mg kg(-1) and RPDpred = 5.0 for Cr; R(2)p = 0.91, 
      RMSEP = 0.2832 mg kg(-1) and RPDpred = 3.4 for Pb; R(2)p = 0.90, 
      RMSEP = 0.2992 mg kg(-1), and RPDpred = 3.3 for Hg. Descent prediction was 
      obtained also for Cd (R(2)p = 0.87, RMSEP = 0.3435 mg kg(-1), and RPDpred = 2.7). 
      To further assess the robustness of the DBN, it was compared with conventional 
      machine learning methods such as support vector machine for regression (SVR), k 
      nearest neighbor (KNN), and partial least squares (PLS). The overall results 
      indicated that the Vis-NIR technique coupled with Boruta and DBN could be 
      reliable and accurate for screening heavy metals in organic fertilizers.
CI  - Copyright (c) 2021 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Guindo, Mahamed Lamine
AU  - Guindo ML
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, PR China.
FAU - Kabir, Muhammad Hilal
AU  - Kabir MH
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, PR China.
FAU - Chen, Rongqin
AU  - Chen R
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, PR China.
FAU - Liu, Fei
AU  - Liu F
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, 866 
      Yuhangtang Road, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy 
      Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China. 
      Electronic address: fliu@zju.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20211120
PL  - Netherlands
TA  - Ecotoxicol Environ Saf
JT  - Ecotoxicology and environmental safety
JID - 7805381
SB  - IM
OTO - NOTNLM
OT  - Boruta
OT  - Deep belief network
OT  - Heavy-metals
OT  - Organic fertilizer
OT  - Vis-NIR
EDAT- 2021/11/24 06:00
MHDA- 2021/11/24 06:00
CRDT- 2021/11/23 20:18
PHST- 2021/08/06 00:00 [received]
PHST- 2021/11/02 00:00 [revised]
PHST- 2021/11/14 00:00 [accepted]
PHST- 2021/11/24 06:00 [pubmed]
PHST- 2021/11/24 06:00 [medline]
PHST- 2021/11/23 20:18 [entrez]
AID - S0147-6513(21)01108-8 [pii]
AID - 10.1016/j.ecoenv.2021.112996 [doi]
PST - aheadofprint
SO  - Ecotoxicol Environ Saf. 2021 Nov 20;228:112996. doi: 
      10.1016/j.ecoenv.2021.112996.