基于可见-近红外光谱的茄子叶绿素荧光参数Fv/Fm预测方法.pdf

s Vol No pp M Spectroscopy and Spectral Analysis September V n N 0 s S S z Z E f Z j S v 0 1 j j h j L i 1 8 j L i 1 K 1 s S v S m I T Y 1 S X V S v S m L M 1 S v S m E i 5 L C N 0 4 B V n S v S m Z E k 3 N 0 V n P 5 E MCS A s Z E o 4 E i y K l B PLSR Z E 4 K o F s Q BP f RBF K ELM B SVR E S v S m Y V 7 K Z E F s S v S m Z E T V N 0 Q q S v S m 9 F A t V Q S v S m V F k P n MSC S M M SNV 1 a F E g E CARS SPA o r T K MSC CARS SPA PLSR SNV CARS SPA PLSR k s Y Z PLSR H Z E 4 o o T V Z E r 4 o o y CSVR y r T K SNV CARS SPA SVR K k Z 8 SNV CARS SPA SVR y Z E 4 r V n S v S m 4 L C Z E Z E V T 3 y j f 4 r m 1 o M s o V n E N 0 m s O D S M A Q V j issn l S E 1 S 8 9 TSCXL NY g S 9 YF NC n S n T e M 3 j S v 0 V 3 e mail leoli nwafu edu cn Y e mail hujin nwsuaf edu cn s T T W Q d l s s S v S m d PS Q K v r q P q K B W I Zhou S v S m I N 0 Hazrati V s v R S v S m U i C S v S m F 7 L C S v S m y V 3 1 i l 7 d S v S m Z T a E L C L H 6 s N N L S v S m L v S p y M M 3 S Z Z S s Q 1 3 H l s Q q s C l DVI nm M 1 K S v S m M 1 r Ibarakip q y B PRI d Q L j S v j j h j L i Z m L NF Pindstrup Sub strate s A N 0 M 3 r B B N 0 v V n Q V n Q d o S nm N OFS Ocean Optics S X HL Ocean Optics S s o SpectroClip TR Ocean Optics S 9 X min Y V n Y Q q 9 T T U V V b V w V b T Q q V Q V w I Q H N s P L z T s NMini Pam II Walz S s P a C C s a min Y V 8 h M s S v S m T S v S m A Z E s n o 1 nm o S o s 5 Z E Monte Carlo sampling method MCS s A s s k s Y h N 3 2 V M 3 Y r 4 a s Y P SG savitzky golay SG n multiplicative scatter correction MSC S M M standard normal variate transformation SNV Z E o 4 Q o L O o V h o M O 4 g E successive projections algorithm SPA K E random frog RF 1 a F E competitive a daptive reweighted sampling CARS F o SPA B 4 Z E Y V g s c K K l L o F 4 RF CARS 5 n Y V y K a 4 M RF E Y V 9 V o 4 q T o 4 S 7CARS 5 V D l o K o F o F M 4 8 n D s L C I n H V h o 4 CARS RF s Y SPA 5 CARS RF 4 o F 7 P SPA M Z E 4 o F o Q q s S v S m PLSR E y Z RMSE K a Z E K o F N o F o 1 y PLSR o 1 9 T v o v w o w T o v PLSR E T v o B v v o 1 B y Z E N K y Z E s E S v S m Y i PLSR 1 V 7 y E s Y BP back propagation neural network BP RBF ra dial basis function neural network RBF K ex treme learning machine ELM B support vector regression SVR k S v S m L Z RMSE r T BP W f tansig f trainlm S K v Q q RBF S f ELM f sigmod SVR f f Y V E 5 p f t T s S v S m u W s 0 u W S v S m 0 u W N 0 Q m U R p nm P C Q q s nm i Q q V n o W C Q q 6 H C 6 S v S m 9 F Q q 8 t nm u t Q A s S v S m PS Q K v r q S v S m v l s l S Q S v t n z y r z r n r p z o 4 y r T K o F o 4 PLSR y o V U Z E MSC SNV V C z k r RMSE 7SG r T B RF SPA Z E 4 o y r T 1 l 4 6 r T Q 7 S y V M Z E Y a n o y r Y z q r y n p p n p q r q v s s r r r p r v t n q n v n o y r p r r v t z r u q Z E M k 构 RMSE I RMSE RAW uk 浇 吵 SPA n uk 浇 吵 RF n uk 浇 吵 CARS n uk 浇 吵 RF SPA n uk 浇 吵 CARS SPA n uk 浇 吵 SG uk 浇 吵 SPA n uk 浇 吵 RF n uk 浇 吵 CARS n uk 浇 吵 RF SPA n uk 浇 吵 CARS SPA n uk 浇 吵 MSC uk 浇 吵 SPA n uk 浇 吵 RF n uk 浇 吵 CARS 倐 uk 浇 吵 RF SPA Y uk 浇 吵 CARS SPA n uk 浇 吵 SNV uk 浇 吵 SPA n uk 浇 吵 RF n uk 浇 吵 CARS n uk 浇 吵 RF SPA Y uk 浇 吵 CARS SPA n uk 浇 吵 s V SG V r 2 H 9 h s r V M E P T V CARS SPA r T z CARS 4 o y K SPA Q 4 o K CARS 4 K 7SPA 5 M W K l RF E V C D y o 4 q M I n o W F r T 7CARS SPA 5 o F P s 1 CARS SPA CARS M o CARS Z E MSC CARS SPA SNV CARS SPA o 4 r T K MSC CARS SPA PLSR k RMSE 7SNV CARS SPA PLSR k M 7 o y o Z E v H 4 o M T K o F B Z E o s f 1 s o s 1 m U m a b s Y MSC SNV m m c d 5 MSC CARS SPA SNV CARS SPA Z E o s L V U o m a b V Z E 4 o V n nm u nm MSC CARS SPA s 1 7SNV CARS SPA s 1 m c d V C s V n u A u MSC CARS SPA V n S o SNV CARS SPA 6 Z E nm S 4 o M S MSC CARS SPA 4 o s Y nm 7SNV CARS SPA o nm o 9 M o 9 v 5 6 t i nm r K v MSC CARS SPA nm o 7SNV CARS SPA nm V o S v S m Q K A V o Q q M K A H 7 H V z Q s c c q c Y 3 1 3 N o K v m o s 1 a MSC b SNV c MSC CARS SPA o s d SNV CARS SPA o s S v t P u n n p r v v p n r y r t u q v v o v n q r v t u v a Spectrum preprocessed by MSC b Spectrum preprocessed by SNV c Weight proportions of characteristic wavelenths selected by MSC CARS SPA d Weight proportions of characteristic wavelenths selected by SNV CARS SPA s y 1 o F Q q s S v S m s Y P E y i PLSR E 1 BP ELM E 4 E Q Z N T V U V V M 1 PLSR V y Z E a n o y r Z q r y n p p n p q r q v s s r r z q r y v t z r u q Z E k 崓 RMSE RMSE MSC CARS SPA PLSR 鼢 I 憫噰 儋舷 BP 鼢 I 憫噰 儋舷 RBF 鼢 I 憫噰 儋舷 ELM 鼢 I 憫噰 儋舷 SVR 鼢 I 憫噰 儋舷 SNV CARS SPA PLSR 鼢 I 憫噰 儋舷 BP 鼢 I 憫噰 儋舷 RBF 鼢 I 憫噰 儋舷 ELM 鼢 I 憫噰 儋舷 SVR 鼢 I 憫噰 儋舷 m c y E T a b S v t S v v t r y s r q v p v z q r y p p v o n r q c a Training set b Testing set A 4 6 d L E 6 ELM k 4 i V E C A k E SVR y r T K l 8 C V C K K l Z W SNV CARS SPA Z E o i SVR y N 0 S v S m r T K z k E T s Y m a b U RMSE k RMSE O o 7 PLSR E y k RMSE V M Z E r S v S m K M 1 o F OSVR E a y S v S m 1 N 0 V n s s S v S m P Z E M 4 E o E y Y V 1 K Z E y E 1 Y V k S v S m N 0 Q s C Q q S v S m 9 F t nm o C K A SG MSC SNV Y V SPA RF CARS CARS SPA RF SPA 4 o F i P PLSR y y CMSC CARS SPA SNV CARS SPA Z E 4 o F y r T K z O o o s V V n u o A u V o S v S m D v 6 o H o K v S v S m Q K A o s Y P BP RBF ELM SVR E y CSNV CARS SPA SVR Z T y K k RMSE PLSR V o Z E E r 4 d 9 Z E V n N 0 s S v S m r Z T E B w i i F k Transactions of the Chinese Society of Agricul tural Engineering j Mohammadi Moghaddam T Razavi S M A Taghizadeh M et al Journal of Food Measurement and Characterization Li H Xu Q Liang Y Analytica Chimica Acta Li H Liang Y Xu Q et al Analytica Chimica Acta ZHAO Heng qian ZHANG Wen bo ZHU Xiao xin et al u f p m Spectroscopy and Spectral Analysis s r q v p v s R t t y n Y r n s S S z O n r q c v V r p p LI Bin GAO Pan FENG Pan CHEN Dan yan ZHANG Hai hui HU Jin College of Mechanical and Electronic Engineering Northwest A s